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A system for detecting high impact-low frequency mutations in primary tumors and metastases

Oncogene volume 37, pages 185–196 (2018)Cite this article

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Abstract

Tumor complexity and intratumor heterogeneity contribute to subclonal diversity. Despite advances in next-generation sequencing (NGS) and bioinformatics, detecting rare mutations in primary tumors and metastases contributing to subclonal diversity is a challenge for precision genomics. Here, in order to identify rare mutations, we adapted a recently described epithelial reprograming assay for short-term propagation of epithelial cells from primary and metastatic tumors. Using this approach, we expanded minor clones and obtained epithelial cell-specific DNA/RNA for quantitative NGS analysis. Comparative Ampliseq Comprehensive Cancer Panel sequence analyses were performed on DNA from unprocessed breast tumor and tumor cells propagated from the same tumor. We identified previously uncharacterized mutations present only in the cultured tumor cells, a subset of which has been reported in brain metastatic but not primary breast tumors. In addition, whole-genome sequencing identified mutations enriched in liver metastases of various cancers, including Notch pathway mutations/chromosomal inversions in 5/5 liver metastases, irrespective of cancer types. Mutations/rearrangements in FHIT, involved in purine metabolism, were detected in 4/5 liver metastases, and the same four liver metastases shared mutations in 32 genes, including mutations of different HLA-DR family members affecting OX40 signaling pathway, which could impact the immune response to metastatic cells. Pathway analyses of all mutated genes in liver metastases showed aberrant tumor necrosis factor and transforming growth factor signaling in metastatic cells. Epigenetic regulators including KMT2C/MLL3 and ARID1B, which are mutated in >50% of hepatocellular carcinomas, were also mutated in liver metastases. Thus, irrespective of cancer types, organ-specific metastases may share common genomic aberrations. Since recent studies show independent evolution of primary tumors and metastases and in most cases mutation burden is higher in metastases than primary tumors, the method described here may allow early detection of subclonal somatic alterations associated with metastatic progression and potentially identify therapeutically actionable, metastasis-specific genomic aberrations.

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Acknowledgements

We thank tissue collection team at the IU Simon Cancer Center, Clinical Research Office and Neurooncology Center at IU School of Medicine for collection of fresh tissues for the study. We also thank the flow cytometry core at the IU Simon Cancer Center. Excellent support from New York Genome Center, particularly Mr Benjamin Hubert, is highly appreciated. IUPUI Signature Center for the Cure of Glioblastoma supported brain metastases tissue collection. Susan G Komen for the Cure (SAC110025 to HN), Indiana CTSI Project development pilot grant (to HN, LL and KNP) and IU Simon Cancer Center Breast Cancer Program Pilot grant (to YL and HN) supported this study. This study utilized core services by National Institutes of Health Grant P30 DK097512 to the Indiana University School of Medicine.

Author contributions

MA, primary cell culturing, DNA and RNA extraction and flow cytometry; YH, RareVar development, bioinformatics and analyses of genomic data; ERS, bioinformatics and analyses of genomic data; PN, primary cell collection and DNA/RNA extraction; JN, digital droplet PCR; SAT, digital droplet PCR, RGM, digital droplet PCR assay design and implementation; AAC, patient accrual, collection and processing of brain metastases; MRS, clinical protocol development and brain metastases collection; LL, bioinformatics and analyses of genomic data; FF, pathway analyses of genomic data; KPN, pathway analyses of genomic data and manuscript writing; KDM, clinical protocol development and implementation of liver metastasis sample collection; YL, assay design, supervision of bioinformatics efforts and manuscript writing; HN, experimental design, primary cell culturing, data interpretation, manuscript writing and overall supervision of the project. All authors read and approve the manuscript.

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Author notes

  1. M Anjanappa and Y Hao: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA

    M Anjanappa, P Bhat-Nakshatri & H Nakshatri

  2. Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, IN, USA

    Y Hao, E R Simpson, L Li & Y Liu

  3. Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA

    J B Nelson, S A Tersey, R G Mirmira & M R Saadatzadeh

  4. Department of Neurosurgery, Indiana University School of Medicine, Indianapolis, IN, USA

    A A Cohen-Gadol

  5. Department of Medical and Molecular Genetics, Indiana University School of Medicine, IN, USA

    L Li & Y Liu

  6. Medical Science Program, Indiana University, Bloomington, IN, USA

    F Fang & K P Nephew

  7. Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA

    K D Miller

  8. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA

    H Nakshatri

  9. Roudebush VA Medical Center, Indianapolis, IN, USA

    H Nakshatri

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  1. M Anjanappa
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Correspondence to Y Liu or H Nakshatri.

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Anjanappa, M., Hao, Y., Simpson, E. et al. A system for detecting high impact-low frequency mutations in primary tumors and metastases. Oncogene 37, 185–196 (2018). https://doi.org/10.1038/onc.2017.322

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