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Annals of Surgical Oncology

, Volume 25, Issue 8, pp 2261–2270 | Cite as

RNA-Seq of Circulating Tumor Cells in Stage II–III Breast Cancer

  • Julie E. LangEmail author
  • Alexander Ring
  • Tania Porras
  • Pushpinder Kaur
  • Victoria A. Forte
  • Neal Mineyev
  • Debu Tripathy
  • Michael F. Press
  • Daniel Campo
Breast Oncology

Abstract

Background

We characterized the whole transcriptome of circulating tumor cells (CTCs) in stage II–III breast cancer to evaluate correlations with primary tumor biology.

Methods

CTCs were isolated from peripheral blood (PB) via immunomagnetic enrichment followed by fluorescence-activated cell sorting (IE/FACS). CTCs, PB, and fresh tumors were profiled using RNA-seq. Formalin-fixed, paraffin-embedded (FFPE) tumors were subjected to RNA-seq and NanoString PAM50 assays with risk of recurrence (ROR) scores.

Results

CTCs were detected in 29/33 (88%) patients. We selected 21 cases to attempt RNA-seq (median number of CTCs = 9). Sixteen CTC samples yielded results that passed quality-control metrics, and these samples had a median of 4,311,255 uniquely mapped reads (less than PB or tumors). Intrinsic subtype predicted by comparing estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) versus PAM50 for FFPE tumors was 85% concordant. However, CTC RNA-seq subtype assessed by the PAM50 classification genes was highly discordant, both with the subtype predicted by ER/PR/HER2 and by PAM50 tumors. Two patients died of metastatic disease, both of whom had high ROR scores and high CTC counts. We identified significant genes, canonical pathways, upstream regulators, and molecular interaction networks comparing CTCs by various clinical factors. We also identified a 75-gene signature with highest expression in CTCs and tumors taken together that was prognostic in The Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium datasets.

Conclusion

It is feasible to use RNA-seq of CTCs in non-metastatic patients to discover novel tumor biology characteristics.

Notes

Acknowledgement

This project was supported by a Society of Surgical Oncology Clinical Investigator Award, a California Breast Cancer Research Program IDEA award, and a STOP Cancer Marni Levine Memorial Seed Grant (JL). The project was also supported in part by grant UL1TR001855 from the National Center for Advancing Translational Science (NCATS) and grant P30CA014089 from the National Cancer Institute of the US National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. Magnetic cell separators were kindly made by the laboratory of Maciej Zborowski (Cleveland Clinic) based on the design provided by BD Biosciences.

Supplementary material

10434_2018_6540_MOESM1_ESM.pdf (35 kb)
Supplementary material 1 (PDF 35 kb)
10434_2018_6540_MOESM2_ESM.pdf (17 kb)
Supplementary material 2 (PDF 17 kb)
10434_2018_6540_MOESM3_ESM.pdf (17 kb)
Supplementary material 3 (PDF 17 kb)
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Supplementary material 4 (PDF 58 kb)
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ELECTRONIC SUPPLEMENTARY Fig. 1 (a–d) Differential gene expression in CTCs based on hormone receptor expression in primary tumors. Heatmaps showing unsupervised hierarchical clustering of individual samples (CTCs or PTs) and volcano plots showing grouped (n = 16 CTCs and n = 12 PTs for a–c, n = 4 CTCs and PTs each for d) differential gene expression (p < 0.05) for: (a) TN samples vs. others (ER-positive or HER2-positive) [n = 512]; (b) ER-positive vs. others (HER2-positive or TNBC) [n = 115 genes]; (c) HER2-positive vs. others (ER-positive or TNBC) [n = 245 genes]; (d) pCR vs. no pCR (n = 65 genes) [color legend: maroon indicates TNBC, orange indicates ER-positive samples, teal indicates HER2-positive samples, green indicates pCR yes, gray indicates remaining samples in each comparison] (TIFF 2881 kb)
10434_2018_6540_MOESM6_ESM.pdf (52 kb)
Supplementary material 6 (PDF 52 kb)
10434_2018_6540_MOESM7_ESM.tiff (2.6 mb)
ELECTRONIC SUPPLEMENTARY Fig. 2 xCell gene signature-based classification of sample cellular composition. The cell type probability for each sample is shown in heatmap format. A score was calculated using the xCell script based on RNA-seq normalized gene expression (logRPKM + 1) in each sample (CTCs, PTs and PB) [color coding: blue indicates high score = high probability, white indicates low score = low probability] (TIFF 2702 kb)
10434_2018_6540_MOESM8_ESM.pdf (113 kb)
ELECTRONIC SUPPLEMENTARY Fig. 3 NanoString results for ER (ESR1), PR (PGR), HER2 (ERBB2), and Ki67 (MKI67). A heatmap shows results for n = 13 FFPE tumors subjected to NanoString assays to characterize their biomarker profiles (PDF 113 kb)

REFERENCES

  1. 1.
    Gupta GP, Massague J. Cancer metastasis: building a framework. Cell. 2006;127(4):679–695.CrossRefPubMedGoogle Scholar
  2. 2.
    Cristofanilli M, Budd GT, Ellis MJ, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med. 2004;351(8):781–791.CrossRefPubMedGoogle Scholar
  3. 3.
    Lucci A, Hall CS, Lodhi AK, et al. Circulating tumour cells in non-metastatic breast cancer: a prospective study. Lancet Oncol. 2012;13(7):688–695.CrossRefPubMedGoogle Scholar
  4. 4.
    Rack B, Schindlbeck C, Juckstock J, et al. Circulating tumor cells predict survival in early average-to-high risk breast cancer patients. J Natl Cancer Inst. 2014;106(5): pii: dju066.Google Scholar
  5. 5.
    Zhang L, Riethdorf S, Wu G, et al. Meta-analysis of the prognostic value of circulating tumor cells in breast cancer. Clin Cancer Res. 2012;18(20):5701–5710.CrossRefPubMedGoogle Scholar
  6. 6.
    Budd GT, Cristofanilli M, Ellis MJ, et al. Circulating tumor cells versus imaging: predicting overall survival in metastatic breast cancer. Clin Cancer Res. 2006;12(21):6403–6409.CrossRefPubMedGoogle Scholar
  7. 7.
    Liu MC, Shields PG, Warren RD, et al. Circulating tumor cells: a useful predictor of treatment efficacy in metastatic breast cancer. J Clin Oncol. 2009;27(31):5153–5159.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Lang JE, Scott JH, Wolf DM, et al. Expression profiling of circulating tumor cells in metastatic breast cancer. Breast Cancer Res Treat. 2015;149(1):121–31.CrossRefPubMedGoogle Scholar
  9. 9.
    Boral D, Vishnoi M, Liu HN, et al. Molecular characterization of breast cancer CTCs associated with brain metastasis. Nat Commun. 2017;8(1):196.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Yu M, Bardia A, Wittner BS, et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science. 2013;339(6119):580–584.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zheng Y, Miyamoto DT, Wittner BS, et al. Expression of β-globin by cancer cells promotes cell survival during blood-borne dissemination. Nat Commun. 2017;8:14344.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Magbanua MJ, Park JW. Isolation of circulating tumor cells by immunomagnetic enrichment and fluorescence-activated cell sorting (IE/FACS) for molecular profiling. Methods. 2013;64(2):114–8.CrossRefPubMedGoogle Scholar
  13. 13.
    Lang JE, Magbanua MJ, Scott JH, et al. A comparison of RNA amplification techniques at sub-nanogram input concentration. BMC Genom. 2009;10:326.CrossRefGoogle Scholar
  14. 14.
    Ring A, Mineyev N, Zhu W, et al. EpCAM based capture detects and recovers circulating tumor cells from all subtypes of breast cancer except claudin-low. Oncotarget. 2015;6(42):44623–44634.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–1184.CrossRefPubMedGoogle Scholar
  16. 16.
    Hammond ME, Hayes DF, Dowsett M, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28(16):2784–2795.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Geiss GK, Bumgarner RE, Birditt B, et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008;26(3):317–325.CrossRefPubMedGoogle Scholar
  18. 18.
    Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–1167.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gendoo DM, Ratanasirigulchai N, Schroder MS, et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics. 2016;32(7):1097–1099.CrossRefPubMedGoogle Scholar
  20. 20.
    Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 15 Oct 2017
  21. 21.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.CrossRefPubMedGoogle Scholar
  23. 23.
    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–140.CrossRefPubMedGoogle Scholar
  24. 24.
    Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Kanehisa M, Goto S, Kawashima S, Nakaya A. The KEGG databases at GenomeNet. Nucleic Acids Res. 2002;30(1):42–46.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.Google Scholar
  27. 27.
    Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery. 2012;2(5):401–404.CrossRefPubMedGoogle Scholar
  28. 28.
    Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–352.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.CrossRefGoogle Scholar
  30. 30.
    Powell AA, Talasaz AH, Zhang H, et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PloS One. 2012;7(5):e33788.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Gulbahce N, Magbanua MJM, Chin R, et al. Quantitative Whole Genome Sequencing of Circulating Tumor Cells Enables Personalized Combination Therapy of Metastatic Cancer. Cancer research. 2017;77(16):4530–4541.CrossRefPubMedGoogle Scholar
  32. 32.
    Porras TB, Kaur P, Ring A, Schechter N, Lang JE. Challenges in using liquid biopsies for gene expression profiling. Oncotarget. 2018;9(6):7036–7053.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Fukuda T, Shirane A, Wada-Hiraike O, et al. HAND2-mediated proteolysis negatively regulates the function of estrogen receptor alpha. Mol Med Rep. 2015;12(4):5538–5544.CrossRefPubMedGoogle Scholar
  34. 34.
    Saitoh T, Katoh M. Molecular cloning and characterization of human WNT8A. Int J Oncol. 2001;19(1):123–127.PubMedGoogle Scholar
  35. 35.
    Nakanishi Y, Walter K, Spoerke JM, et al. Activating Mutations in PIK3CB Confer Resistance to PI3 K Inhibition and Define a Novel Oncogenic Role for p110beta. Cancer Res. 2016;76(5):1193–1203.CrossRefPubMedGoogle Scholar
  36. 36.
    Elebro K, Borgquist S, Rosendahl AH, et al. High estrogen receptor beta expression is prognostic among adjuvant chemotherapy-treated patients-results from a population-based breast cancer cohort. Clin Cancer Res. 2017;23(3):766–777.CrossRefPubMedGoogle Scholar
  37. 37.
    Mazel M, Jacot W, Pantel K, et al. Frequent expression of PD-L1 on circulating breast cancer cells. Mol Oncol. 2015;9(9):1773–1782.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Kalinich M, Bhan I, Kwan TT, et al. An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma. Proc Natl Acad Sci U S A. 2017;114(5):1123–1128.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Miyamoto DT, Zheng Y, Wittner BS, et al. RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science. 2015;349(6254):1351–1356.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society of Surgical Oncology 2018

Authors and Affiliations

  • Julie E. Lang
    • 1
    Email author
  • Alexander Ring
    • 2
  • Tania Porras
    • 1
  • Pushpinder Kaur
    • 1
  • Victoria A. Forte
    • 3
  • Neal Mineyev
    • 1
  • Debu Tripathy
    • 4
  • Michael F. Press
    • 5
  • Daniel Campo
    • 6
  1. 1.Section of Surgical Oncology, Department of Surgery and University of Southern California Norris Cancer CenterUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of OncologyUniversity Hospital ZurichZurichSwitzerland
  3. 3.Division of Medical Oncology, Department of MedicineMaimonides Medical CenterNew YorkUSA
  4. 4.Department of Breast Medical OncologyUT MD Anderson Cancer CenterHoustonUSA
  5. 5.Department of Pathology and University of Southern California Norris Cancer CenterUniversity of Southern CaliforniaLos AngelesUSA
  6. 6.Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA

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