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Multiple Myeloma, Gammopathies

Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors

Leukemia volume 30, pages 1094–1102 (2016)Cite this article

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Abstract

Multiple myeloma (MM) is characterized by significant genetic diversity at subclonal levels that have a defining role in the heterogeneity of tumor progression, clinical aggressiveness and drug sensitivity. Although genome profiling studies have demonstrated heterogeneity in subclonal architecture that may ultimately lead to relapse, a gene expression-based prediction program that can identify, distinguish and quantify drug response in sub-populations within a bulk population of myeloma cells is lacking. In this study, we performed targeted transcriptome analysis on 528 pre-treatment single cells from 11 myeloma cell lines and 418 single cells from 8 drug-naïve MM patients, followed by intensive bioinformatics and statistical analysis for prediction of proteasome inhibitor sensitivity in individual cells. Using our previously reported drug response gene expression profile signature at the single-cell level, we developed an R Statistical analysis package available at https://github.com/bvnlabSCATTome, SCATTome (single-cell analysis of targeted transcriptome), that restructures the data obtained from Fluidigm single-cell quantitative real-time-PCR analysis run, filters missing data, performs scaling of filtered data, builds classification models and predicts drug response of individual cells based on targeted transcriptome using an assortment of machine learning methods. Application of SCATT should contribute to clinically relevant analysis of intratumor heterogeneity, and better inform drug choices based on subclonal cellular responses.

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References

  1. Rajkumar SV . Multiple myeloma: 2013 update on diagnosis, risk-stratification, and management. Am J Hematol 2013; 88: 226–235.

    Article  PubMed  Google Scholar 

  2. Mitsiades CS, Davies FE, Laubach JP, Joshua D, San Miguel J, Anderson KC et al. Future directions of next-generation novel therapies, combination approaches, and the development of personalized medicine in myeloma. J Clin Oncol 2011; 29: 1916–1923.

    Article  PubMed  Google Scholar 

  3. Munshi NC, Anderson KC . New strategies in the treatment of multiple myeloma. Clin Cancer Res 2013; 19: 3337–3344.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Moreau P, Richardson PG, Cavo M, Orlowski RZ, San Miguel JF, Palumbo A et al. Proteasome inhibitors in multiple myeloma: 10 years later. Blood 2012; 120: 947–959.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Potts BC, Albitar MX, Anderson KC, Baritaki S, Berkers C, Bonavida B et al. Marizomib, a proteasome inhibitor for all seasons: preclinical profile and a framework for clinical trials. Curr Cancer Drug Targets 2011; 11: 254–284.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kortuem KM, Stewart AK . Carfilzomib. Blood 2013; 121: 893–897.

    Article  CAS  PubMed  Google Scholar 

  7. Allegra A, Alonci A, Gerace D, Russo S, Innao V, Calabro L et al. New orally active proteasome inhibitors in multiple myeloma. Leuk Res 2014; 38: 1–9.

    Article  CAS  PubMed  Google Scholar 

  8. Kuehl WM, Bergsagel PL . Molecular pathogenesis of multiple myeloma and its premalignant precursor. J Clin Invest 2012; 122: 3456–3463.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fonseca R, Bergsagel PL, Drach J, Shaughnessy J, Gutierrez N, Stewart AK et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 2009; 23: 2210–2221.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Richardson PG, Sonneveld P, Schuster MW, Irwin D, Stadtmauer EA, Facon T et al. Safety and efficacy of bortezomib in high-risk and elderly patients with relapsed multiple myeloma. Br J Haematol 2007; 137: 429–435.

    Article  CAS  PubMed  Google Scholar 

  11. Vangsted A, Klausen TW, Vogel U . Genetic variations in multiple myeloma II: association with effect of treatment. Eur J Haematol 2012; 88: 93–117.

    Article  CAS  PubMed  Google Scholar 

  12. Kumar S, Rajkumar SV . Many facets of bortezomib resistance/susceptibility. Blood 2008; 112: 2177–2178.

    Article  CAS  PubMed  Google Scholar 

  13. Navin N, Krasnitz A, Rodgers L, Cook K, Meth J, Kendall J et al. Inferring tumor progression from genomic heterogeneity. Genome Res 2010; 20: 68–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E et al. Clonal competition with alternating dominance in multiple myeloma. Blood 2012; 120: 1067–1076.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Mroz EA, Tward AD, Pickering CR, Myers JN, Ferris RL, Rocco JW . High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 2013; 119: 3034–3042.

    Article  PubMed  Google Scholar 

  16. Bolli N, Avet-Loiseau H, Wedge DC, Van Loo P, Alexandrov LB, Martincorena I et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun 2014; 5: 2997.

    Article  PubMed  Google Scholar 

  17. Lohr JG, Stojanov P, Carter SL, Cruz-Gordillo P, Lawrence MS, Auclair D et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 2014; 25: 91–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Melchor L, Brioli A, Wardell CP, Murison A, Potter NE, Kaiser MF et al. Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia 2014; 28: 1705–1715.

    Article  CAS  PubMed  Google Scholar 

  19. Kim KT, Lee HW, Lee HO, Kim SC, Seo YJ, Chung W et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol 2015; 16: 015–0692-3.

    Article  Google Scholar 

  20. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014; 344: 1396–1401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ennen M, Keime C, Kobi D, Mengus G, Lipsker D, Thibault-Carpentier C et al. Single-cell gene expression signatures reveal melanoma cell heterogeneity. Oncogene 2015; 34: 3251–3263.

    Article  CAS  PubMed  Google Scholar 

  22. Zhan F, Barlogie B, Mulligan G, Shaughnessy Jr JD, Bryant B . High-risk myeloma: a gene expression based risk-stratification model for newly diagnosed multiple myeloma treated with high-dose therapy is predictive of outcome in relapsed disease treated with single-agent bortezomib or high-dose dexamethasone. Blood 2008; 111: 968–969.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Shaughnessy Jr JD, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 2007; 109: 2276–2284.

    Article  CAS  PubMed  Google Scholar 

  24. Kuiper R, Broyl A, de Knegt Y, van Vliet MH, van Beers EH, van der Holt B et al. A gene expression signature for high-risk multiple myeloma. Leukemia 2012; 26: 2406–2413.

    Article  CAS  PubMed  Google Scholar 

  25. Moreaux J, Klein B, Bataille R, Descamps G, Maiga S, Hose D et al. A high-risk signature for patients with multiple myeloma established from the molecular classification of human myeloma cell lines. Haematologica 2011; 96: 574–582.

    Article  CAS  PubMed  Google Scholar 

  26. Stessman HA, Baughn LB, Sarver A, Xia T, Deshpande R, Mansoor A et al. Profiling bortezomib resistance identifies secondary therapies in a mouse myeloma model. Mol Cancer Ther 2013; 12: 1140–1150.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Stessman HA, Mansoor A, Zhan F, Linden MA, Van Ness B, Baughn LB . Bortezomib resistance can be reversed by induced expression of plasma cell maturation markers in a mouse in vitro model of multiple myeloma. PLoS One 2013; 8: e77608.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mitra AK, Crews KR, Pounds S, Cao X, Feldberg T, Ghodke Y et al. Genetic variants in cytosolic 5′-nucleotidase II are associated with its expression and cytarabine sensitivity in HapMap cell lines and in patients with acute myeloid leukemia. J Pharmacol Exp Ther 2011; 339: 9–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hastie T, Tibshirani R, Friedman J . The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn Springer: New York, NY, USA, 2009.

    Book  Google Scholar 

  30. Barzi F, Woodward M . Imputations of missing values in practice: results from imputations of serum cholesterol in 28 cohort studies. Am J Epidemiol 2004; 160: 34–45.

    Article  PubMed  Google Scholar 

  31. Dempster AP, Laird NM, Rubin DB . Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 1977; 39: 1–38.

    Google Scholar 

  32. Honaker J, King G, Blackwell M, Amelia II . A program for missing data. J Stat Softw 2011; 45: 1.

    Article  Google Scholar 

  33. Tibshirani R . Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B 2011; 73: 273–282.

    Article  Google Scholar 

  34. Breiman L . Random Forests. Mach Learning 2001; 45: 5–32.

    Article  Google Scholar 

  35. Steinwart I, Christmann A . Support Vector Machines. Springer: New York, NY, USA, 2008.

    Google Scholar 

  36. Hanley JA, McNeil BJ . The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29–36.

    Article  CAS  PubMed  Google Scholar 

  37. Bradley AP . The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 1997; 30: 1145–1159.

    Article  Google Scholar 

  38. Kisselev AF, van der Linden WA, Overkleeft HS . Proteasome inhibitors: an expanding army attacking a unique target. Chem Biol 2012; 19: 99–115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. LeBlanc R, Catley LP, Hideshima T, Lentzsch S, Mitsiades CS, Mitsiades N et al. Proteasome inhibitor PS-341 inhibits human myeloma cell growth in vivo and prolongs survival in a murine model. Cancer Res 2002; 62: 4996–5000.

    CAS  PubMed  Google Scholar 

  40. Mulligan G, Mitsiades C, Bryant B, Zhan F, Chng WJ, Roels S et al. Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood 2007; 109: 3177–3188.

    Article  CAS  PubMed  Google Scholar 

  41. Rajkumar SV, Dispenzieri A . Evaluation and monitoring of response to therapy in multiple myeloma. Haematologica 2005; 90: 1305–1308.

    PubMed  Google Scholar 

  42. Palumbo A, Anderson K . Multiple myeloma. N Engl J Med 2011; 364: 1046–1060.

    Article  CAS  PubMed  Google Scholar 

  43. Magrangeas F, Avet-Loiseau H, Gouraud W, Lode L, Decaux O, Godmer P et al. Minor clone provides a reservoir for relapse in multiple myeloma. Leukemia 2013; 27: 473–481.

    Article  CAS  PubMed  Google Scholar 

  44. Walker BA, Wardell CP, Melchor L, Brioli A, Johnson DC, Kaiser MF et al. Intraclonal heterogeneity is a critical early event in the development of myeloma and precedes the development of clinical symptoms. Leukemia 2014; 28: 384–390.

    Article  PubMed  Google Scholar 

  45. Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC et al. Initial genome sequencing and analysis of multiple myeloma. Nature 2011; 471: 467–472.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Egan JB, Shi CX, Tembe W, Christoforides A, Kurdoglu A, Sinari S et al. Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood 2012; 120: 1060–1066.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Morgan GJ, Walker BA, Davies FE . The genetic architecture of multiple myeloma. Nat Rev Cancer 2012; 12: 335–348.

    Article  CAS  PubMed  Google Scholar 

  48. Stessman HA, Lulla A, Xia T, Mitra A, Harding T, Mansoor A et al. High-throughput drug screening identifies compounds and molecular strategies for targeting proteasome inhibitor-resistant multiple myeloma. Leukemia 2014; 28: 2263–2267.

    Article  CAS  PubMed  Google Scholar 

  49. Barrena S, Almeida J, Yunta M, Lopez A, Fernandez-Mosteirin N, Giralt M et al. Aberrant expression of tetraspanin molecules in B-cell chronic lymphoproliferative disorders and its correlation with normal B-cell maturation. Leukemia 2005; 19: 1376–1383.

    Article  CAS  PubMed  Google Scholar 

  50. Rasmussen AM, Blomhoff HK, Stokke T, Horejsi V, Smeland EB . Cross-linking of CD53 promotes activation of resting human B lymphocytes. J Immunol 1994; 153: 4997–5007.

    CAS  PubMed  Google Scholar 

  51. Mariani O, Brennetot C, Coindre JM, Gruel N, Ganem C, Delattre O et al. JUN oncogene amplification and overexpression block adipocytic differentiation in highly aggressive sarcomas. Cancer Cell 2007; 11: 361–374.

    Article  CAS  PubMed  Google Scholar 

  52. Man SM, Zhu Q, Zhu L, Liu Z, Karki R, Malik A et al. Critical role for the DNA sensor AIM2 in stem cell proliferation and cancer. Cell 2015; 162: 45–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Baumeister W, Walz J, Zuhl F, Seemuller E . The proteasome: paradigm of a self-compartmentalizing protease. Cell 1998; 92: 367–380.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by a grant from the Minnesota Partnership for Biotechnology and Medical Genomics to BVN, SK and JJ. AKM was supported by a grant from the International Myeloma Foundation. We gratefully acknowledge the expert technical support from the University of Minnesota Genomics Center and Mayo Clinic Center for Individualized Medicine and the members of the Genome Analysis Core for support with the Single-Cell RNAseq. We thank Takeda Pharmaceuticals and Amgen for the drugs.

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Authors and Affiliations

  1. Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA

    A K Mitra, T Harding, H Stessman & B Van Ness

  2. School of Statistics, University of Minnesota, Minneapolis, MN, USA

    U K Mukherjee

  3. Medical Genome Facility Genome Analysis Core, Mayo Clinic, Rochester, MN, USA

    J S Jang & J Jen

  4. Department of Health Science Research, Mayo Clinic, Rochester, MN, USA

    Y Li & A Abyzov

  5. Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA

    S Kumar & V Rajkumar

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Correspondence to B Van Ness.

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Mitra, A., Mukherjee, U., Harding, T. et al. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia 30, 1094–1102 (2016). https://doi.org/10.1038/leu.2015.361

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