Skip to main content

Thank you for visiting nature.com. 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.

  • Technical Report
  • Published:

Chromatin immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles

Nature Medicine volume 22, pages 685–691 (2016)Cite this article

Subjects

Abstract

Extensive cross-linking introduced during routine tissue fixation of clinical pathology specimens severely hampers chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) analysis from archived tissue samples. This limits the ability to study the epigenomes of valuable, clinically annotated tissue resources. Here we describe fixed-tissue chromatin immunoprecipitation sequencing (FiT-seq), a method that enables reliable extraction of soluble chromatin from formalin-fixed paraffin-embedded (FFPE) tissue samples for accurate detection of histone marks. We demonstrate that FiT-seq data from FFPE specimens are concordant with ChIP-seq data from fresh-frozen samples of the same tumors. By using multiple histone marks, we generate chromatin-state maps and identify cis-regulatory elements in clinical samples from various tumor types that can readily allow us to distinguish between cancers by the tissue of origin. Tumor-specific enhancers and superenhancers that are elucidated by FiT-seq analysis correlate with known oncogenic drivers in different tissues and can assist in the understanding of how chromatin states affect gene regulation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Development of FiT-seq, and its effect on strength and resolution of histone mark signals.
Figure 2: Differences in H3K4me2 marking in CRC tissue and matched normal colon mucosa.
Figure 3: Additional histone marks mapped by FiT-seq.
Figure 4: Tumor-specific enhancers and H3K4me2-defined superenhancers identified in multiple cancer types.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Garraway, L.A. & Lander, E.S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

    CAS  PubMed  Google Scholar 

  2. Fanelli, M. et al. Pathology tissue–chromatin immunoprecipitation, coupled with high-throughput sequencing, allows the epigenetic profiling of patient samples. Proc. Natl. Acad. Sci. USA 107, 21535–21540 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Shi, S.R., Imam, S.A., Young, L., Cote, R.J. & Taylor, C.R. Antigen retrieval immunohistochemistry under the influence of pH using monoclonal antibodies. J. Histochem. Cytochem. 43, 193–201 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. Shi, S.R., Liu, C., Perez, J. & Taylor, C.R. Protein-embedding technique: a potential approach to standardization of immunohistochemistry for formalin-fixed, paraffin-embedded tissue sections. J. Histochem. Cytochem. 53, 1167–1170 (2005).

    Article  CAS  PubMed  Google Scholar 

  6. Yamashita, S. Heat-induced antigen retrieval: mechanisms and application to histochemistry. Prog. Histochem. Cytochem. 41, 141–200 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Shin, H., Liu, T., Duan, X., Zhang, Y. & Liu, X.S. Computational methodology for ChIP-seq analysis. Quant. Biol. 1, 54–70 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Anders, S., Pyl, P.T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. Anders, S. & Huber, W. Differential expression analysis for sequence-count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lee, T.I. et al. Control of developmental regulators by Polycomb in human embryonic stem cells. Cell 125, 301–313 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ben-Porath, I. et al. An embryonic stem-cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hansen, K.D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Balasubramanian, D. et al. H3K4me3 inversely correlates with DNA methylation at a large class of non-CpG-island-containing start sites. Genome Med. 4, 47 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  15. Carroll, J.S. et al. Genome-wide analysis of estrogen-receptor-binding sites. Nat. Genet. 38, 1289–1297 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Verzi, M.P. et al. TCF4 and CDX2, major transcription factors for intestinal function, converge on the same cis-regulatory regions. Proc. Natl. Acad. Sci. USA 107, 15157–15162 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Verzi, M.P. et al. Differentiation-specific histone modifications reveal dynamic chromatin interactions and partners for the intestinal transcription factor CDX2. Dev. Cell 19, 713–726 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hatzis, P. et al. Genome-wide pattern of TCF7L2 (TCF4) chromatin occupancy in colorectal cancer cells. Mol. Cell. Biol. 28, 2732–2744 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. van Bragt, M.P., Hu, X., Xie, Y. & Li, Z. RUNX1, a transcription factor mutated in breast cancer, controls the fate of ER-positive mammary luminal cells. eLife 3, e03881 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Santagata, S., Ligon, K.L. & Hornick, J.L. Embryonic stem cell transcription factor signatures in the diagnosis of primary and metastatic germ cell tumors. Am. J. Surg. Pathol. 31, 836–845 (2007).

    Article  PubMed  Google Scholar 

  23. Zhang, X., Zhang, J., Wang, T., Esteban, M.A. & Pei, D. Esrrb activates Oct4 transcription and sustains self-renewal and pluripotency in embryonic stem cells. J. Biol. Chem. 283, 35825–35833 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Heng, J.C. et al. The nuclear receptor Nr5a2 can replace Oct4 in the reprogramming of murine somatic cells to pluripotent cells. Cell Stem Cell 6, 167–174 (2010).

    Article  CAS  PubMed  Google Scholar 

  25. Hnisz, D. et al. Superenhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Zentner, G.E. & Scacheri, P.C. The chromatin fingerprint of gene enhancer elements. J. Biol. Chem. 287, 30888–30896 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ong, C.T. & Corces, V.G. Enhancer function: new insights into the regulation of tissue-specific gene expression. Nat. Rev. Genet. 12, 283–293 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kouros-Mehr, H., Slorach, E.M., Sternlicht, M.D. & Werb, Z. GATA-3 maintains the differentiation of the luminal cell fate in the mammary gland. Cell 127, 1041–1055 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jiang, J. et al. A core Klf circuitry regulates self-renewal of embryonic stem cells. Nat. Cell Biol. 10, 353–360 (2008).

    Article  PubMed  Google Scholar 

  30. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).

  31. Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ramírez, F., Dündar, F., Diehl, S., Grüning, B.A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Liu, T. et al. Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol. 12, R83 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. McLean, C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the DFCI–Novartis Drug Discovery Program (R.A.S.), the US NIH grant P50CA127003 (R.A.S.), the Susan F. Smith Center for Women's Cancers (M. Brown), grants from the Susan G. Komen and the Breast Cancer Research foundations (M. Brown), the Medical Oncology Translational Grant Program from the Dana-Farber Cancer Institute (C.J.S.), a grant from Instituto de Salud Carlos III the Spanish Economy and Competitiveness Ministry (grant PI13-01818; P.C.), the ConSEPOC–Comunidad de Madrid (grant S2010/BMD-2542; P.C.), and fellowships from the Asociación Española Contra el Cáncer (Programa de Formación Avanzada en Oncología 2010; P.C.) and the Fundación Caja Madrid (P.C.). We thank E. Díaz and M. de Miguel for technical assistance.

Author information

Authors and Affiliations

  1. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Paloma Cejas, Lewyn Li, Melissa Duarte, Prakash Rao, X Shirley Liu, Myles Brown, Ramesh A Shivdasani & Henry W Long

  2. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA

    Paloma Cejas, Lewyn Li, Nicholas K O'Neill, Melissa Duarte, Prakash Rao, Joaquim Bellmunt, Stephanie Mullane, Christopher J Sweeney, Myles Brown, Ramesh A Shivdasani & Henry W Long

  3. Department of Medical Oncology, Hospital La Paz Institute for Health Research (IdiPAZ), Madrid, Spain

    Paloma Cejas, Marta Mendiola & Jaime Feliu

  4. Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Michaela Bowden & Chensheng W Zhou

  5. Department of Pathology, Hospital La Paz Institute for Health Research (IdiPAZ), Madrid, Spain

    Marta Mendiola & Emilio Burgos

  6. Precision Oncology Laboratory, Infanta Sofia University Hospital, Madrid, Spain

    Juan Moreno-Rubio

  7. Department of Surgery, Jimenez Diaz Foundation, Madrid, Spain

    Héctor Guadalajara & Damián García-Olmo

  8. Clinical Research Unit, Jimenez Diaz Foundation, Madrid, Spain

    Víctor Moreno

  9. Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Michelle Hirsch & Andrea Richardson

  10. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA

    X Shirley Liu

Authors
  1. Paloma Cejas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lewyn Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicholas K O'Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Melissa Duarte
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Prakash Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michaela Bowden
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chensheng W Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marta Mendiola
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Emilio Burgos
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jaime Feliu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Juan Moreno-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Héctor Guadalajara
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Víctor Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Damián García-Olmo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Joaquim Bellmunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Stephanie Mullane
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Michelle Hirsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Christopher J Sweeney
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Andrea Richardson
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. X Shirley Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Myles Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Ramesh A Shivdasani
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Henry W Long
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.C., R.A.S., M. Brown and H.W.L. conceived and designed the study; L.L., P.C., H.W.L. and N.K.O'N. performed computational and statistical analyses; M.D., P.R., M. Bowden and C.W.Z. acquired data; E.B. and M.M. reviewed CRC pathology; J.F., J.M.-R., H.G., V.M., S.M., J.B., D.G.-O., C.J.S., M.H. and A.R. supervised collection of tumor samples; X.S.L., M. Brown, H.W.L. and R.A.S. provided overall supervision; and P.C., R.A.S. and H.W.L. drafted the manuscript, with input from all of the authors.

Corresponding authors

Correspondence to Ramesh A Shivdasani or Henry W Long.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 3763 kb)

Supplementary Table 1

Sample characteristics, study design, and ChIP-seq library statistics. (XLSX 28 kb)

Supplementary Table 2

Comparison of PAT-ChIP-seq and FiT-seq performance on FFPE samples. (XLSX 25 kb)

Supplementary Table 3

Genes differentially H3K4me2 marked at promoters between normal and tumoral colorectal samples. (XLSX 1866 kb)

Supplementary Table 4

Gene Set Enrichment Analysis of the differentially H3K4me2 marked promoters between normal and tumoral colorectal samples for both the FF and FFPE cohorts. (XLSX 1682 kb)

Supplementary Table 5

Motif analysis at the differentially marked enhancers between normal and tumoral colorectal samples. (XLSX 55 kb)

Supplementary Table 6

Locations of the most discriminant enhancers across-tumortypes as determined by ANOVA. (XLSX 340 kb)

Supplementary Table 7

Motif analysis at the most discriminant enhancers acrosstumor-types. (XLSX 97 kb)

Supplementary Table 8

List of super-enhancers called for the series of tumors included in the cross tumor study in Figure 4. (XLSX 9396 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cejas, P., Li, L., O'Neill, N. et al. Chromatin immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles. Nat Med 22, 685–691 (2016). https://doi.org/10.1038/nm.4085

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm.4085

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer