Association of JAK2-V617F Mutations Detected by Solid Tumor Sequencing With Coexistent Myeloproliferative Neoplasms | Hematology | JAMA Oncology | JAMA Network
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Table.  Clinical and Sequencing Data for Patients With Detected JAK2-V617F Mutation
Clinical and Sequencing Data for Patients With Detected JAK2-V617F Mutation
1.
Ptashkin  RN, Mandelker  DL, Coombs  CC,  et al.  Prevalence of clonal hematopoiesis mutations in tumor-only clinical genomic profiling of solid tumors.  JAMA Oncol. 2018;4(11):1589-1593. doi:10.1001/jamaoncol.2018.2297PubMedGoogle Scholar
2.
Severson  EA, Riedlinger  GM, Connelly  CF,  et al.  Detection of clonal hematopoiesis of indeterminate potential in clinical sequencing of solid tumor specimens.  Blood. 2018;131(22):2501-2505. doi:10.1182/blood-2018-03-840629PubMedGoogle ScholarCrossref
3.
Li  SD, Ma  M, Li  H,  et al.  Cancer gene profiling in non-small cell lung cancers reveals activating mutations in JAK2 and JAK3 with therapeutic implications.  Genome Med. 2017;9(1):89. doi:10.1186/s13073-017-0478-1PubMedGoogle ScholarCrossref
4.
Rumi  E, Cazzola  M.  Diagnosis, risk stratification, and response evaluation in classical myeloproliferative neoplasms.  Blood. 2017;129(6):680-692. doi:10.1182/blood-2016-10-695957PubMedGoogle ScholarCrossref
5.
Hadigol  M, Khiabanian  H.  MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.  BMC Bioinformatics. 2018;19(1):219. doi:10.1186/s12859-018-2223-1PubMedGoogle ScholarCrossref
6.
Rabadan  R, Bhanot  G, Marsilio  S, Chiorazzi  N, Pasqualucci  L, Khiabanian  H.  On statistical modeling of sequencing noise in high depth data to assess tumor evolution.  J Stat Phys. 2018;172(1):143-155. doi:10.1007/s10955-017-1945-1PubMedGoogle ScholarCrossref
Research Letter
January 3, 2019

Association of JAK2-V617F Mutations Detected by Solid Tumor Sequencing With Coexistent Myeloproliferative Neoplasms

Author Affiliations
  • 1Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
  • 2Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey
  • 3Department of Pathology and Laboratory Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey
  • 4Department of Medicine, Rutgers Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey
JAMA Oncol. 2019;5(2):265-267. doi:10.1001/jamaoncol.2018.6286

Clinical sequencing assays aim to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most clinical-grade implementations lack patient-matched germline DNA, and supplemental analyses are needed to infer the mutational status of variants. In addition, genomic heterogeneity confounds the ability to distinguish subclonal tumor alterations from those possibly originating from the microenvironment’s nontumor component. Recent studies have shown that certain mutations identified in sequencing assays did not reflect alterations in the tumor but instead revealed alterations in infiltrating hematopoietic cells possibly from undiagnosed clonal hematopoiesis of indeterminate potential (CHIP),1,2 an age-related expansion of hematopoietic stem cells harboring somatic mutations predominantly in DNMT3A (GenBank 1788), TET2 (GenBank 54790), and ASXL1 (GenBank 171023). Mutations in other genes associated with hematologic diseases are detected less frequently in CHIP, and when these mutations are encountered, reports have attributed them either to the tumor or to CHIP.1-3

We examined whether detection of these mutations might indicate the presence of other disorders. Specifically, the JAK2-V617F mutation has been directly linked to myeloproliferative neoplasms (MPNs), and detection of this mutation, along with activating exon 12 JAK2 (GenBank 3717) mutations and alterations in MPL (GenBank 4352) or CALR (GenBank 811) can establish a diagnosis of MPN.4

Methods

We analyzed clinical sequencing data from 2030 solid tumors tested at Rutgers University (New Brunswick, NJ) between November 1, 2012, and August 31, 2018. Specimens from 8 patients (5 men and 3 women; median age, 74 years [range, 60-82 years]) had JAK2-V617F mutations, but on examination, mutations were present at variant allele frequencies (VAFs) significantly different from those expected from tumor purity (Table). Three patients had available specimens. To validate that JAK2 mutations existed in hematopoietic elements, we performed manual macrodissection on paraffin-embedded specimens to enrich for cancer or hematopoietic cells and sequenced these samples at high depth (>2500×) using a 49-gene panel (RainDance Technologies) on Illumina MiSeq. We identified all sites that were different from the reference using an inclusive variant caller5 and used a Bayesian approach to detect true mutations against background error6 in which mutations were tested in each sample against 33 previously sequenced, JAK2 wild-type samples. After correcting for multiple hypotheses using the Benjamini-Hochberg method, we generated a list of variants with a false-discovery rate less than 0.001. Clinical records and patient samples for this study were obtained under approval from Rutgers University Institutional Review Board protocol 2012002075 with written consent (4 of 8 patients) and protocol 20170001364 with waived consent (4 of 8 patients).

Results

Examination of patients’ clinical histories indicated that 4 of the 8 patients with JAK2-V617F mutations detected on solid tumor sequencing had a diagnosis of MPN; 2 had polycythemia vera, 1 had essential thrombocythemia, and 1 had myelofibrosis. One additional patient had a platelet count of 529 × 103/μL (to convert to cells ×109 per liter, multiply by 1.0) before initiating chemotherapy, whereas interpretation of the results of blood tests for the 3 other patients without a diagnosis of MPN was confounded by receipt of concurrent chemotherapy. In the validation analysis of the macrodissected specimens, 2 of the 3 patients had JAK2-V617F in lymphocyte-enriched samples at significantly higher VAFs (5.6% at 2807× and 28.1% at 3029×, respectively) than in tumor-enriched samples (3.5% at 3264× and 5.9% at 1772×, respectively) (P < .001, Fisher exact test). For these patients, tumor-specific mutations were all detected at significantly higher VAFs in tumor-enriched samples. In 1 patient, CHIP-associated mutations in U2AF1 (GenBank 7307) and TET2 were also detected at significantly higher VAFs in the lymphocyte-enriched sample. In the third patient without a prior diagnosis of MPN, JAK2-V617F was detected at a significantly higher VAF in the tumor-enriched sample (12% at 1788× vs 2.2% at 2757×). In this patient, a CHIP-associated mutation in DNMT3A was detected at a significantly higher VAF in the lymphocyte-enriched sample, suggesting that JAK2-V617F was likely present in tumor cells (Table).

Discussion

There have been conflicting reports in the literature as to whether JAK2-V617F detected by solid tumor sequencing is associated with a mutation in the tumor or CHIP.1,3 Our analysis suggests that although both of these results are possible, detection of JAK2-V617F may instead be associated with a coexistent MPN. Limitations may arise in distinguishing underlying JAK2-V617F mutations from CHIP versus those from an MPN in patients receiving chemotherapy for their solid tumors. Therefore, when MPN-associated mutations are observed in solid tumor sequencing data, caution is necessary for proper patient treatment, and a hematologic workup should be considered in the appropriate clinical context.

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Article Information

Accepted for Publication: October 29, 2018.

Corresponding Author: Hossein Khiabanian, PhD, Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ 08903 (h.khiabanian@rutgers.edu).

Published Online: January 3, 2019. doi:10.1001/jamaoncol.2018.6286

Author Contributions: Drs Khiabanian and Ganesan had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Khiabanian and Ganesan contributed equally to this work.

Concept and design: Riedlinger, Khiabanain, Ganesan.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Hadigol, Khiabanain.

Obtained funding: Khiabanain.

Administrative, technical, or material support: Riedlinger, Khiabanain, Ganesan.

Supervision: Khiabanain, Ganesan.

Conflict of Interest Disclosures: Dr Riedlinger reported serving on a scientific advisory board and as consultant to Personal Genome Diagnostics. Dr Ganesan reported serving on a scientific advisory board and as consultant for Inspirata Inc, holding patents on digital imaging technology licensed to Inspirata Inc, holding equity in Inspirata Inc, serving on an advisory board for Novartis Pharmaceuticals, and serving as a consultant for Roche. No other disclosures were reported.

Funding/Support. This research was supported by grant P30CA072720 from the National Cancer Institute for the Comprehensive Genomics, Biomedical Informatics, and Biospecimen Repository Shared Resources at Rutgers Cancer Institute of New Jersey. and grant 1S10OD012346-01A1 from National Institutes of Health for the Rutgers Office of Advanced Research Computing, as well as pilot project funding from the Department of Pathology and Laboratory Medicine at Rutgers Robert Wood Johnson Medical School.

Role of the Funder/Sponsor: The funding sources had no 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.
Ptashkin  RN, Mandelker  DL, Coombs  CC,  et al.  Prevalence of clonal hematopoiesis mutations in tumor-only clinical genomic profiling of solid tumors.  JAMA Oncol. 2018;4(11):1589-1593. doi:10.1001/jamaoncol.2018.2297PubMedGoogle Scholar
2.
Severson  EA, Riedlinger  GM, Connelly  CF,  et al.  Detection of clonal hematopoiesis of indeterminate potential in clinical sequencing of solid tumor specimens.  Blood. 2018;131(22):2501-2505. doi:10.1182/blood-2018-03-840629PubMedGoogle ScholarCrossref
3.
Li  SD, Ma  M, Li  H,  et al.  Cancer gene profiling in non-small cell lung cancers reveals activating mutations in JAK2 and JAK3 with therapeutic implications.  Genome Med. 2017;9(1):89. doi:10.1186/s13073-017-0478-1PubMedGoogle ScholarCrossref
4.
Rumi  E, Cazzola  M.  Diagnosis, risk stratification, and response evaluation in classical myeloproliferative neoplasms.  Blood. 2017;129(6):680-692. doi:10.1182/blood-2016-10-695957PubMedGoogle ScholarCrossref
5.
Hadigol  M, Khiabanian  H.  MERIT reveals the impact of genomic context on sequencing error rate in ultra-deep applications.  BMC Bioinformatics. 2018;19(1):219. doi:10.1186/s12859-018-2223-1PubMedGoogle ScholarCrossref
6.
Rabadan  R, Bhanot  G, Marsilio  S, Chiorazzi  N, Pasqualucci  L, Khiabanian  H.  On statistical modeling of sequencing noise in high depth data to assess tumor evolution.  J Stat Phys. 2018;172(1):143-155. doi:10.1007/s10955-017-1945-1PubMedGoogle ScholarCrossref
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