Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5


  1. 1

    Prasad V, Fojo T, Brada M . Precision oncology: origins, optimism, and potential. Lancet Oncol 2016; 17: e81–e86.

    Article  Google Scholar 

  2. 2

    Collins DC, Sundar R, Lim JS, Yap TA . Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics. Trends Pharmacol Sci 2017; 38: 25–40.

    CAS  Article  Google Scholar 

  3. 3

    Sholl LM, Do K, Shivdasani P, Cerami E, Dubuc AM, Kuo FC et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight 2016; 1: e87062.

    Article  Google Scholar 

  4. 4

    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366: 883–892.

    CAS  Article  Google Scholar 

  5. 5

    Miller CA, Gindin Y, Lu C, Griffith OL, Griffith M, Shen D et al. Aromatase inhibition remodels the clonal architecture of estrogen-receptor-positive breast cancers. Nature Communications 2016; 7: 12498.

    CAS  Article  Google Scholar 

  6. 6

    Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med 2015; 21: 751–759.

    CAS  Article  Google Scholar 

  7. 7

    Kostadinov R, Maley CC, Kuhner MK . Bulk Genotyping of Biopsies Can Create Spurious Evidence for Hetereogeneity in Mutation Content. PLoS Comput Biol 2016; 12: e1004413.

    Article  Google Scholar 

  8. 8

    Brastianos PK, Carter SL, Santagata S, Cahill DP, Taylor-Weiner A, Jones RT et al. Genomic Characterization of Brain Metastases Reveals Branched Evolution and Potential Therapeutic Targets. Cancer Discovery 2015; 5: 1164–1177.

    CAS  Article  Google Scholar 

  9. 9

    Harper KL, Sosa MS, Entenberg D, Hosseini H, Cheung JF, Nobre R et al. Mechanism of early dissemination and metastasis in Her2+ mammary cancer. Nature 2016 doi:10.1038/nature20609.

    CAS  Article  Google Scholar 

  10. 10

    Hosseini H, Obradovic MM, Hoffmann M, Harper KL, Sosa MS, Werner-Klein M et al. Early dissemination seeds metastasis in breast cancer. Nature 2016 doi:10.1038/nature20785.

    CAS  Article  Google Scholar 

  11. 11

    McDonald OG, Li X, Saunders T, Tryggvadottir R, Mentch SJ, Warmoes MO et al. Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis. Nat Genet 2017; 49: 367–376.

    CAS  Article  Google Scholar 

  12. 12

    Liu X, Ory V, Chapman S, Yuan H, Albanese C, Kallakury B et al. ROCK inhibitor and feeder cells induce the conditional reprogramming of epithelial cells. Am J Pathol 2012; 180: 599–607.

    CAS  Article  Google Scholar 

  13. 13

    Brodt P . Role of the Microenvironment in Liver Metastasis: From Pre- to Prometastatic Niches. Clin Cancer Res 2016; 22: 5971–5982.

    CAS  Article  Google Scholar 

  14. 14

    Nakshatri H, Anjanappa M, Bhat-Nakshatri P . Ethnicity-Dependent and -Independent Heterogeneity in Healthy Normal Breast Hierarchy Impacts Tumor Characterization. Scientific Reports 2015; 5: 13526.

    CAS  Article  Google Scholar 

  15. 15

    Cancer Genome Atlas Research N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 2014; 513: 202–209.

    Article  Google Scholar 

  16. 16

    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discovery 2012; 2: 401–404.

    Article  Google Scholar 

  17. 17

    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling 2013; 6: pl1.

    Article  Google Scholar 

  18. 18

    Pepe C, Guidugli L, Sensi E, Aretini P, D'Andrea E, Montagna M et al. Methyl group metabolism gene polymorphisms as modifier of breast cancer risk in Italian BRCA1/2 carriers. Breast Cancer Res Treat 2007; 103: 29–36.

    CAS  Article  Google Scholar 

  19. 19

    Flowers M, Birkey Reffey S, Mertz SA . Marc Hurlbert for the Metastatic Breast Cancer A. Obstacles, Opportunities and Priorities for Advancing Metastatic Breast. Cancer Research. Cancer Res 2017; 77: 3386–3390.

    CAS  Article  Google Scholar 

  20. 20

    Visvader JE, Stingl J . Mammary stem cells and the differentiation hierarchy: current status and perspectives. Genes Dev 2014; 28: 1143–1158.

    CAS  Article  Google Scholar 

  21. 21

    Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF . Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A 2003; 100: 3983–3988.

    CAS  Article  Google Scholar 

  22. 22

    Sheridan C, Kishimoto H, Fuchs RK, Mehrotra S, Bhat-Nakshatri P, Turner CH et al. CD44+/CD24- breast cancer cells exhibit enhanced invasive properties: an early step necessary for metastasis. Breast Cancer Res 2006; 8: R59.

    Article  Google Scholar 

  23. 23

    Kim J, Villadsen R, Sorlie T, Fogh L, Gronlund SZ, Fridriksdottir AJ et al. Tumor initiating but differentiated luminal-like breast cancer cells are highly invasive in the absence of basal-like activity. Proc Natl Acad Sci U S A 2012; 109: 6124–6129.

    CAS  Article  Google Scholar 

  24. 24

    Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490: 61–70.

    Article  Google Scholar 

  25. 25

    Sebestyen E, Zawisza M, Eyras E . Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer. Nucleic Acids Res 2015; 43: 1345–1356.

    CAS  Article  Google Scholar 

  26. 26

    Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 2012; 486: 400–404.

    CAS  Article  Google Scholar 

  27. 27

    Fujimoto A, Totoki Y, Abe T, Boroevich KA, Hosoda F, Nguyen HH et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nat Genet 2012; 44: 760–764.

    CAS  Article  Google Scholar 

  28. 28

    Chmielecki J, Crago AM, Rosenberg M, O'Connor R, Walker SR, Ambrogio L et al. Whole-exome sequencing identifies a recurrent NAB2-STAT6 fusion in solitary fibrous tumors. Nat Genet 2013; 45: 131–132.

    CAS  Article  Google Scholar 

  29. 29

    Robinson DR, Wu YM, Kalyana-Sundaram S, Cao X, Lonigro RJ, Sung YS et al. Identification of recurrent NAB2-STAT6 gene fusions in solitary fibrous tumor by integrative sequencing. Nat Genet 2013; 45: 180–185.

    CAS  Article  Google Scholar 

  30. 30

    Gruber TA, Downing JR . The biology of pediatric acute megakaryoblastic leukemia. Blood 2015; 126: 943–949.

    CAS  Article  Google Scholar 

  31. 31

    Kumar R, Manning J, Spendlove HE, Kremmidiotis G, McKirdy R, Lee J et al. ZNF652, a novel zinc finger protein, interacts with the putative breast tumor suppressor CBFA2 T3 to repress transcription. Mol Cancer Res 2006; 4: 655–665.

    CAS  Article  Google Scholar 

  32. 32

    van Bon BW, Oortveld MA, Nijtmans LG, Fenckova M, Nijhof B, Besseling J et al. CEP89 is required for mitochondrial metabolism and neuronal function in man and fly. Human Molecular Genetics 2013; 22: 3138–3151.

    CAS  Article  Google Scholar 

  33. 33

    Vaque JP, Dorsam RT, Feng X, Iglesias-Bartolome R, Forsthoefel DJ, Chen Q et al. A genome-wide RNAi screen reveals a Trio-regulated Rho GTPase circuitry transducing mitogenic signals initiated by G protein-coupled receptors. Mol Cell 2013; 49: 94–108.

    CAS  Article  Google Scholar 

  34. 34

    Chen D, Huang X, Cai J, Guo S, Qian W, Wery JP et al. A set of defined oncogenic mutation alleles seems to better predict the response to cetuximab in CRC patient-derived xenograft than KRAS 12/13 mutations. Oncotarget 2015; 6: 40815–40821.

    PubMed  PubMed Central  Google Scholar 

  35. 35

    Park JT, Johnson N, Liu S, Levesque M, Wang YJ, Ho H et al. Differential in vivo tumorigenicity of diverse KRAS mutations in vertebrate pancreas: A comprehensive survey. Oncogene 2015; 34: 2801–2806.

    CAS  Article  Google Scholar 

  36. 36

    Odore E, Lokiec F, Cvitkovic E, Bekradda M, Herait P, Bourdel F et al. Phase I Population Pharmacokinetic Assessment of the Oral Bromodomain Inhibitor OTX015 in Patients with Haematologic Malignancies. Clin Pharmacokinet 2016; 55: 397–405.

    CAS  Article  Google Scholar 

  37. 37

    Bafna S, Kaur S, Batra SK . Membrane-bound mucins: the mechanistic basis for alterations in the growth and survival of cancer cells. Oncogene 2010; 29: 2893–2904.

    CAS  Article  Google Scholar 

  38. 38

    Mitchell RA, Metz CN, Peng T, Bucala R . Sustained mitogen-activated protein kinase (MAPK) and cytoplasmic phospholipase A2 activation by macrophage migration inhibitory factor (MIF). Regulatory role in cell proliferation and glucocorticoid action. J Biol Chem 1999; 274: 18100–18106.

    CAS  Article  Google Scholar 

  39. 39

    Hu X, Lu H, Cao S, Deng YL, Li QJ, Wan Q et al. Stem cells derived from human first-trimester umbilical cord have the potential to differentiate into oocyte-like cells in vitro. Int J Mol Med 2015; 35: 1219–1229.

    CAS  Article  Google Scholar 

  40. 40

    Petit FM, Serres C, Bourgeon F, Pineau C, Auer J . Identification of sperm head proteins involved in zona pellucida binding. Hum Reprod 2013; 28: 852–865.

    CAS  Article  Google Scholar 

  41. 41

    Cormier S, Vandormael-Pournin S, Babinet C, Cohen-Tannoudji M . Developmental expression of the Notch signaling pathway genes during mouse preimplantation development. Gene Expr Patterns 2004; 4: 713–717.

    CAS  Article  Google Scholar 

  42. 42

    Giampieri R, Scartozzi M, Loretelli C, Piva F, Mandolesi A, Lezoche G et al. Cancer stem cell gene profile as predictor of relapse in high risk stage II and stage III, radically resected colon cancer patients. PloS One 2013; 8: e72843.

    CAS  Article  Google Scholar 

  43. 43

    Steeg PS, Camphausen KA, Smith QR . Brain metastases as preventive and therapeutic targets. Nat Rev Cancer 11: 352–363.

    CAS  Article  Google Scholar 

  44. 44

    Burnett RM, Craven KE, Krishnamurthy P, Goswami CP, Badve S, Crooks P et al. Organ-specific adaptive signaling pathway activation in metastatic breast cancer cells. Oncotarget 2015; 6: 12682–12696.

    Article  Google Scholar 

  45. 45

    Chen L, Liu P, Evans Jr TC, Ettwiller LM . DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science 2017; 355: 752–756.

    CAS  Article  Google Scholar 

  46. 46

    Coomans de Brachene A, Demoulin JB . FOXO transcription factors in cancer development and therapy. Cellular and Molecular Life Sciences: CMLS 2016; 73: 1159–1172.

    CAS  Article  Google Scholar 

  47. 47

    Pereira B, Chin SF, Rueda OM, Vollan HK, Provenzano E, Bardwell HA et al. The somatic mutation profiles of 2433 breast cancers refines their genomic and transcriptomic landscapes. Nature Communications 2016; 7: 11479.

    CAS  Article  Google Scholar 

  48. 48

    Vainchenker W, Kralovics R . Genetic basis and molecular pathophysiology of classical myeloproliferative neoplasms. Blood 2017; 129: 667–679.

    CAS  Article  Google Scholar 

  49. 49

    Eirew P, Steif A, Khattra J, Ha G, Yap D, Farahani H et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 2015; 518: 422–426.

    CAS  Article  Google Scholar 

  50. 50

    Cheung LW, Yu S, Zhang D, Li J, Ng PK, Panupinthu N et al. Naturally occurring neomorphic PIK3R1 mutations activate the MAPK pathway, dictating therapeutic response to MAPK pathway inhibitors. Cancer Cell 2014; 26: 479–494.

    CAS  Article  Google Scholar 

  51. 51

    McCreery MQ, Halliwill KD, Chin D, Delrosario R, Hirst G, Vuong P et al. Evolution of metastasis revealed by mutational landscapes of chemically induced skin cancers. Nat Med 2015; 21: 1514–1520.

    CAS  Article  Google Scholar 

  52. 52

    Cejalvo JM, Martinez de Duenas E, Galvan P, Garcia-Recio S, Burgues Gasion O, Pare L et al. Intrinsic Subtypes and Gene Expression Profiles in Primary and Metastatic Breast Cancer. Cancer Res 2017; 77: 2213–2221.

    CAS  Article  Google Scholar 

  53. 53

    Kishi N, Tang Z, Maeda Y, Hirai A, Mo R, Ito M et al. Murine homologs of deltex define a novel gene family involved in vertebrate Notch signaling and neurogenesis. Int J Dev Neurosci 2001; 19: 21–35.

    CAS  Article  Google Scholar 

  54. 54

    Linch SN, Kasiewicz MJ, McNamara MJ, Hilgart-Martiszus IF, Farhad M, Redmond WL . Combination OX40 agonism/CTLA-4 blockade with HER2 vaccination reverses T-cell anergy and promotes survival in tumor-bearing mice. Proc Natl Acad Sci U S A 2016; 113: E319–E327.

    CAS  Article  Google Scholar 

  55. 55

    Arun B, Kilic G, Yen C, Foster B, Yardley DA, Gaynor R et al. Loss of FHIT expression in breast cancer is correlated with poor prognostic markers. Cancer Epidemiol Biomarkers Prev 2005; 14: 1681–1685.

    CAS  Article  Google Scholar 

  56. 56

    Sinha R, Hussain S, Mehrotra R, Kumar RS, Kumar K, Pande P et al. Kras gene mutation and RASSF1A, FHIT and MGMT gene promoter hypermethylation: indicators of tumor staging and metastasis in adenocarcinomatous sporadic colorectal cancer in Indian population. PloS One 2013; 8: e60142.

    CAS  Article  Google Scholar 

  57. 57

    Yan W, Xu N, Han X, Zhou XM, He B . The clinicopathological significance of FHIT hypermethylation in non-small cell lung cancer, a meta-analysis and literature review. Scientific Reports 2016; 6: 19303.

    CAS  Article  Google Scholar 

  58. 58

    Buxton IL, Yokdang N, Matz RM . Purinergic mechanisms in breast cancer support intravasation, extravasation and angiogenesis. Cancer Lett 2010; 291: 131–141.

    CAS  Article  Google Scholar 

  59. 59

    Jozwik KM, Chernukhin I, Serandour AA, Nagarajan S, Carroll JS . FOXA1 Directs H3K4 Monomethylation at Enhancers via Recruitment of the Methyltransferase MLL3. Cell Reports 2016; 17: 2715–2723.

    CAS  Article  Google Scholar 

  60. 60

    Zhang Z, Christin JR, Wang C, Ge K, Oktay MH, Guo W . Mammary-Stem-Cell-Based Somatic Mouse Models Reveal Breast Cancer Drivers Causing Cell Fate Dysregulation. Cell Reports 2016; 16: 3146–3156.

    CAS  Article  Google Scholar 

  61. 61

    Cheng J, Blum R, Bowman C, Hu D, Shilatifard A, Shen S et al. A role for H3K4 monomethylation in gene repression and partitioning of chromatin readers. Mol Cell 2014; 53: 979–992.

    CAS  Article  Google Scholar 

  62. 62

    Kloosterman WP, Hoogstraat M, Paling O, Tavakoli-Yaraki M, Renkens I, Vermaat JS et al. Chromothripsis is a common mechanism driving genomic rearrangements in primary and metastatic colorectal cancer. Genome Biol 2011; 12: R103.

    CAS  Article  Google Scholar 

  63. 63

    Hao Y, Zhang P, Xuei X, Nakshatri H, Edenberg HJ, Li L et al. Statistical modeling for sensitive detection of low-frequency single nucleotide variants. BMC Genomics 2016; 17 (Suppl 7): 514.

    Article  Google Scholar 

  64. 64

    Hao YXX, Li L, Nakshatri H, Edenberg HJ, Liu Y . A framework for detecting low frequency single nucleotide variants. Journal of Computational Biology 2017; 24: 637–646.

    CAS  Article  Google Scholar 

  65. 65

    Fisher MM, Watkins RA, Blum J, Evans-Molina C, Chalasani N, DiMeglio LA et al. Elevations in Circulating Methylated and Unmethylated Preproinsulin DNA in New-Onset Type 1 Diabetes. Diabetes 2015; 64: 3867–3872.

    CAS  Article  Google Scholar 

Download references


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.

Author information



Corresponding authors

Correspondence to Y Liu or H Nakshatri.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on the Oncogene website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


Quick links