Skip to main content
SpringerLink
Log in

Gene Expression Signatures of the Tumor Microenvironment: Relation to Tumor Phenotypes and Progress in Breast Cancer

  • Chapter
  • First Online:
Biomarkers of the Tumor Microenvironment

  • 1455 Accesses

Abstract

Cancer cell invasion and progression to metastasis have been extensively studied. There is not one all-inclusive model that encompasses the complete picture of the different conditions and pathways operating in human tumors. Application of gene expression signatures is one way of mining the complex tumor landscape and has been proposed to represent a robust method to reflect the many signaling systems.

This chapter gives an update on gene expression signature studies related to breast cancer progress, with particular focus on the supporting stroma. Several signature studies indicate that a combination of extracellular remodeling, activated vascular biology, immune responses, and metabolic reprogramming, in part adipocyte-related, takes place during breast cancer progression. Stromal alterations are likely to be exploited for novel biomarkers and companion treatment targets. Some basic methodological aspects and recent developments are highlighted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fidler IJ. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat Rev Cancer. 2003;3(6):453–8.

    Article  CAS  PubMed  Google Scholar 

  2. Talmadge JE, Fidler IJ. AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res. 2010;70(14):5649–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Paget S. The distribution of secondary growths in cancer of the breast. Lancet. 1889;1:571–3.

    Article  Google Scholar 

  4. Nguyen DX, Massague J. Genetic determinants of cancer metastasis. Nat Rev Genet. 2007;8(5):341–52.

    Article  CAS  PubMed  Google Scholar 

  5. Nguyen DX, Bos PD, Massague J. Metastasis: from dissemination to organ-specific colonization. Nat Rev Cancer. 2009;9(4):274–84.

    Article  CAS  PubMed  Google Scholar 

  6. Bhat R, Bissell MJ. Of plasticity and specificity: dialectics of the microenvironment and macroenvironment and the organ phenotype. Wiley Interdiscip Rev Dev Biol. 2014;3(2):147–63.

    Article  CAS  PubMed  Google Scholar 

  7. Boudreau A, van't Veer LJ, Bissell MJ. An “elite hacker”: breast tumors exploit the normal microenvironment program to instruct their progression and biological diversity. Cell Adhes Migr. 2012;6(3):236–48.

    Article  Google Scholar 

  8. Wculek SK, Malanchi I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature. 2015;528(7582):413–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Valastyan S, Weinberg RA. Tumor metastasis: molecular insights and evolving paradigms. Cell. 2011;147(2):275–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Oppenheimer SB. Cellular basis of cancer metastasis: a review of fundamentals and new advances. Acta Histochem. 2006;108(5):327–34.

    Article  CAS  PubMed  Google Scholar 

  11. Kaplan RN, Rafii S, Lyden D. Preparing the “soil”: the premetastatic niche. Cancer Res. 2006;66(23):11089–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kaplan RN, Riba RD, Zacharoulis S, Bramley AH, Vincent L, Costa C, et al. VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche. Nature. 2005;438(7069):820–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403(6769):503–11.

    Article  CAS  PubMed  Google Scholar 

  14. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(5439):531–7.

    Article  CAS  PubMed  Google Scholar 

  15. Kim H, Nam SW, Rhee H, Shan Li L, Ju Kang H, Hye Koh K, et al. Different gene expression profiles between microsatellite instability-high and microsatellite stable colorectal carcinomas. Oncogene. 2004;23(37):6218–25.

    Article  CAS  PubMed  Google Scholar 

  16. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.

    Article  CAS  PubMed  Google Scholar 

  17. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A. 2003;100(18):10393–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li W, Wang R, Yan Z, Bai L, Sun Z. High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles. PLoS One. 2012;7(3):e33653.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. van ’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6.

    Article  PubMed  Google Scholar 

  21. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671–9.

    Article  CAS  PubMed  Google Scholar 

  22. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999–2009.

    Article  PubMed  Google Scholar 

  23. Rouzier R, Rajan R, Wagner P, Hess KR, Gold DL, Stec J, et al. Microtubule-associated protein tau: a marker of paclitaxel sensitivity in breast cancer. Proc Natl Acad Sci U S A. 2005;102(23):8315–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997;278(5338):680–6.

    Article  CAS  PubMed  Google Scholar 

  25. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, et al. Functional discovery via a compendium of expression profiles. Cell. 2000;102(1):109–26.

    Article  CAS  PubMed  Google Scholar 

  26. Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, et al. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet. 2003;34(2):226–30.

    Article  CAS  PubMed  Google Scholar 

  27. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006;439(7074):353–7.

    Article  CAS  PubMed  Google Scholar 

  28. Black EP, Huang E, Dressman H, Rempel R, Laakso N, Asa SL, et al. Distinct gene expression phenotypes of cells lacking Rb and Rb family members. Cancer Res. 2003;63(13):3716–23.

    CAS  PubMed  Google Scholar 

  29. Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, et al. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell. 2003;114(3):323–34.

    Article  CAS  PubMed  Google Scholar 

  30. Biomarkers Definitions Working G. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95.

    Article  Google Scholar 

  31. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70.

    Article  CAS  PubMed  Google Scholar 

  32. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.

    Article  CAS  PubMed  Google Scholar 

  33. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35.

    Article  CAS  PubMed  Google Scholar 

  34. Lamb J. The Connectivity Map: a new tool for biomedical research. Nat Rev Cancer. 2007;7(1):54–60.

    Article  CAS  PubMed  Google Scholar 

  35. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100(14):8418–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Manjili MH, Najarian K, Wang XY. Signatures of tumor-immune interactions as biomarkers for breast cancer prognosis. Future Oncol. 2012;8(6):703–11.

    Article  CAS  PubMed  Google Scholar 

  38. Zhao C, Wu M, Zeng N, Xiong M, Hu W, Lv W, et al. Cancer-associated adipocytes: emerging supporters in breast cancer. J Exp Clin Cancer Res. 2020;39(1):156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Peng XL, Moffitt RA, Torphy RJ, Volmar KE, Yeh JJ. De novo compartment deconvolution and weight estimation of tumor samples using DECODER. Nat Commun. 2019;10(1):4729.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Allinen M, Beroukhim R, Cai L, Brennan C, Lahti-Domenici J, Huang H, et al. Molecular characterization of the tumor microenvironment in breast cancer. Cancer Cell. 2004;6(1):17–32.

    Article  CAS  PubMed  Google Scholar 

  42. Ma XJ, Dahiya S, Richardson E, Erlander M, Sgroi DC. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res. 2009;11(1):R7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Roman-Perez E, Casbas-Hernandez P, Pirone JR, Rein J, Carey LA, Lubet RA, et al. Gene expression in extratumoral microenvironment predicts clinical outcome in breast cancer patients. Breast Cancer Res. 2012;14(2):R51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Troester MA, Lee MH, Carter M, Fan C, Cowan DW, Perez ER, et al. Activation of host wound responses in breast cancer microenvironment. Clin Cancer Res. 2009;15(22):7020–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Finak G, Bertos N, Pepin F, Sadekova S, Souleimanova M, Zhao H, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14(5):518–27.

    Article  CAS  PubMed  Google Scholar 

  46. Casey T, Bond J, Tighe S, Hunter T, Lintault L, Patel O, et al. Molecular signatures suggest a major role for stromal cells in development of invasive breast cancer. Breast Cancer Res Treat. 2009;114(1):47–62.

    Article  CAS  PubMed  Google Scholar 

  47. Van den Eynden GG, Smid M, Van Laere SJ, Colpaert CG, Van der Auwera I, Bich TX, et al. Gene expression profiles associated with the presence of a fibrotic focus and the growth pattern in lymph node-negative breast cancer. Clin Cancer Res. 2008;14(10):2944–52.

    Article  PubMed  Google Scholar 

  48. Marchini C, Montani M, Konstantinidou G, Orru R, Mannucci S, Ramadori G, et al. Mesenchymal/stromal gene expression signature relates to basal-like breast cancers, identifies bone metastasis and predicts resistance to therapies. PLoS One. 2010;5(11):e14131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Guo W, Keckesova Z, Donaher JL, Shibue T, Tischler V, Reinhardt F, et al. Slug and Sox9 cooperatively determine the mammary stem cell state. Cell. 2012;148(5):1015–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008;133(4):704–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Morel AP, Lievre M, Thomas C, Hinkal G, Ansieau S, Puisieux A. Generation of breast cancer stem cells through epithelial-mesenchymal transition. PLoS One. 2008;3(8):e2888.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Planche A, Bacac M, Provero P, Fusco C, Delorenzi M, Stehle JC, et al. Identification of prognostic molecular features in the reactive stroma of human breast and prostate cancer. PLoS One. 2011;6(5):e18640.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Winslow S, Leandersson K, Edsjo A, Larsson C. Prognostic stromal gene signatures in breast cancer. Breast Cancer Res. 2015;17:23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Desmedt C, Majjaj S, Kheddoumi N, Singhal SK, Haibe-Kains B, El Ouriaghli F, et al. Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment. Clin Cancer Res. 2012;18(4):1004–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rajski M, Zanetti-Dallenbach R, Vogel B, Herrmann R, Rochlitz C, Buess M. IGF-I induced genes in stromal fibroblasts predict the clinical outcome of breast and lung cancer patients. BMC Med. 2010;8:1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Bredholt G, Mannelqvist M, Stefansson IM, Birkeland E, Bo TH, Oyan AM, et al. Tumor necrosis is an important hallmark of aggressive endometrial cancer and associates with hypoxia, angiogenesis and inflammation responses. Oncotarget. 2015;6(37):39676–91.

    Article  PubMed  PubMed Central  Google Scholar 

  57. West RB, Nuyten DS, Subramanian S, Nielsen TO, Corless CL, Rubin BP, et al. Determination of stromal signatures in breast carcinoma. PLoS Biol. 2005;3(6):e187.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bareche Y, Buisseret L, Gruosso T, Girard E, Venet D, Dupont F, et al. Unraveling triple-negative breast cancer tumor microenvironment heterogeneity: towards an optimized treatment approach. J Natl Cancer Inst. 2020;112(7):708–19.

    Article  PubMed  CAS  Google Scholar 

  60. Tamborero D, Rubio-Perez C, Muinos F, Sabarinathan R, Piulats JM, Muntasell A, et al. A pan-cancer landscape of interactions between solid tumors and infiltrating immune cell populations. Clin Cancer Res. 2018;24(15):3717–28.

    Article  CAS  PubMed  Google Scholar 

  61. Qian J, Olbrecht S, Boeckx B, Vos H, Laoui D, Etlioglu E, et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 2020;30(9):745–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2004;2(2):E7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Dvorak HF. Tumors: wounds that do not heal. Similarities between tumor stroma generation and wound healing. N Engl J Med. 1986;315(26):1650–9.

    Article  CAS  PubMed  Google Scholar 

  64. Tchou J, Kossenkov AV, Chang L, Satija C, Herlyn M, Showe LC, et al. Human breast cancer associated fibroblasts exhibit subtype specific gene expression profiles. BMC Med Genet. 2012;5:39.

    CAS  Google Scholar 

  65. Bauer M, Su G, Casper C, He R, Rehrauer W, Friedl A. Heterogeneity of gene expression in stromal fibroblasts of human breast carcinomas and normal breast. Oncogene. 2010;29(12):1732–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Singer CF, Gschwantler-Kaulich D, Fink-Retter A, Haas C, Hudelist G, Czerwenka K, et al. Differential gene expression profile in breast cancer-derived stromal fibroblasts. Breast Cancer Res Treat. 2008;110(2):273–81.

    Article  CAS  PubMed  Google Scholar 

  67. Frings O, Augsten M, Tobin NP, Carlson J, Paulsson J, Pena C, et al. Prognostic significance in breast cancer of a gene signature capturing stromal PDGF signaling. Am J Pathol. 2013;182(6):2037–47.

    Article  CAS  PubMed  Google Scholar 

  68. Sonnenblick A, Salmon-Divon M, Salgado R, Dvash E, Ponde N, Zahavi T, et al. Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer. Int J Cancer. 2020;147(1):266–76.

    Article  CAS  PubMed  Google Scholar 

  69. Siletz A, Kniazeva E, Jeruss JS, Shea LD. Transcription factor networks in invasion-promoting breast carcinoma-associated fibroblasts. Cancer Microenviron. 2013;6(1):91–107.

    Article  CAS  PubMed  Google Scholar 

  70. Bartoschek M, Oskolkov N, Bocci M, Lovrot J, Larsson C, Sommarin M, et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nat Commun. 2018;9(1):5150.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Wu SZ, Roden DL, Wang C, Holliday H, Harvey K, Cazet AS, et al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer. EMBO J. 2020;39(19):e104063.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Woelfle U, Cloos J, Sauter G, Riethdorf L, Janicke F, van Diest P, et al. Molecular signature associated with bone marrow micrometastasis in human breast cancer. Cancer Res. 2003;63(18):5679–84.

    CAS  PubMed  Google Scholar 

  73. Bergamaschi A, Tagliabue E, Sorlie T, Naume B, Triulzi T, Orlandi R, et al. Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome. J Pathol. 2008;214(3):357–67.

    Article  CAS  PubMed  Google Scholar 

  74. Triulzi T, Casalini P, Sandri M, Ratti M, Carcangiu ML, Colombo MP, et al. Neoplastic and stromal cells contribute to an extracellular matrix gene expression profile defining a breast cancer subtype likely to progress. PLoS One. 2013;8(2):e56761.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wallgard E, Larsson E, He L, Hellstrom M, Armulik A, Nisancioglu MH, et al. Identification of a core set of 58 gene transcripts with broad and specific expression in the microvasculature. Arterioscler Thromb Vasc Biol. 2008;28(8):1469–76.

    Article  CAS  PubMed  Google Scholar 

  76. Cleuren ACA, van der Ent MA, Jiang H, Hunker KL, Yee A, Siemieniak DR, et al. The in vivo endothelial cell translatome is highly heterogeneous across vascular beds. Proc Natl Acad Sci U S A. 2019;116(47):23618–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hu Z, Fan C, Livasy C, He X, Oh DS, Ewend MG, et al. A compact VEGF signature associated with distant metastases and poor outcomes. BMC Med. 2009;7:9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Pepin F, Bertos N, Laferriere J, Sadekova S, Souleimanova M, Zhao H, et al. Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes. Breast Cancer Res. 2012;14(4):R120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Bender RJ, Mac GF. Expression of VEGF and semaphorin genes define subgroups of triple negative breast cancer. PLoS One. 2013;8(5):e61788.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wallace JA, Li F, Balakrishnan S, Cantemir-Stone CZ, Pecot T, Martin C, et al. Ets2 in tumor fibroblasts promotes angiogenesis in breast cancer. PLoS One. 2013;8(8):e71533.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Xiao L, Harrell JC, Perou CM, Dudley AC. Identification of a stable molecular signature in mammary tumor endothelial cells that persists in vitro. Angiogenesis. 2014;17(3):511–8.

    Article  CAS  PubMed  Google Scholar 

  82. Chang CW, Tsai CW, Wang HF, Tsai HC, Chen HY, Tsai TF, et al. Identification of a developmentally regulated striatum-enriched zinc-finger gene, Nolz-1, in the mammalian brain. Proc Natl Acad Sci U S A. 2004;101(8):2613–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Matsumoto K, Nishihara S, Kamimura M, Shiraishi T, Otoguro T, Uehara M, et al. The prepattern transcription factor Irx2, a target of the FGF8/MAP kinase cascade, is involved in cerebellum formation. Nat Neurosci. 2004;7(6):605–12.

    Article  CAS  PubMed  Google Scholar 

  84. Mannelqvist M, Stefansson IM, Bredholt G, Hellem Bo T, Oyan AM, Jonassen I, et al. Gene expression patterns related to vascular invasion and aggressive features in endometrial cancer. Am J Pathol. 2011;178(2):861–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Mannelqvist M, Wik E, Stefansson IM, Akslen LA. An 18-gene signature for vascular invasion is associated with aggressive features and reduced survival in breast cancer. PLoS One. 2014;9(6):e98787.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Stefansson IM, Raeder M, Wik E, Mannelqvist M, Kusonmano K, Knutsvik G, et al. Increased angiogenesis is associated with a 32-gene expression signature and 6p21 amplification in aggressive endometrial cancer. Oncotarget. 2015;6(12):10634–45.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Tobin NP, Wennmalm K, Lindstrom LS, Foukakis T, He L, Genove G, et al. An endothelial gene signature score predicts poor outcome in patients with endocrine-treated, low genomic grade breast tumors. Clin Cancer Res. 2016;22(10):2417–26.

    Article  CAS  PubMed  Google Scholar 

  88. Guarischi-Sousa R, Monteiro JS, Alecrim LC, Michaloski JS, Cardeal LB, Ferreira EN, et al. A transcriptome-based signature of pathological angiogenesis predicts breast cancer patient survival. PLoS Genet. 2019;15(12):e1008482.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Harrell JC, Pfefferle AD, Zalles N, Prat A, Fan C, Khramtsov A, et al. Endothelial-like properties of claudin-low breast cancer cells promote tumor vascular permeability and metastasis. Clin Exp Metastasis. 2014;31(1):33–45.

    Article  CAS  PubMed  Google Scholar 

  90. Mendiola M, Martinez-Marin V, Herranz J, Heredia V, Yebenes L, Zamora P, et al. Predictive value of angiogenesis-related gene profiling in patients with HER2-negative metastatic breast cancer treated with bevacizumab and weekly paclitaxel. Oncotarget. 2016;7(17):24217–27.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Kruger K, Silwal-Pandit L, Wik E, Straume O, Stefansson IM, Borgen E, et al. Baseline microvessel density predicts response to neoadjuvant bevacizumab treatment of locally advanced breast cancer. Sci Rep. 2021;11(1):3388.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Pitroda SP, Zhou T, Sweis RF, Filippo M, Labay E, Beckett MA, et al. Tumor endothelial inflammation predicts clinical outcome in diverse human cancers. PLoS One. 2012;7(10):e46104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Oshi M, Newman S, Tokumaru Y, Yan L, Matsuyama R, Endo I, et al. Intra-tumoral angiogenesis is associated with inflammation, immune reaction and metastatic recurrence in breast cancer. Int J Mol Sci. 2020;21(18):6708.

    Article  CAS  PubMed Central  Google Scholar 

  94. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–30.

    Article  CAS  PubMed  Google Scholar 

  95. Joyce JA, Fearon DT. T cell exclusion, immune privilege, and the tumor microenvironment. Science. 2015;348(6230):74–80.

    Article  CAS  PubMed  Google Scholar 

  96. Adams S, Diamond JR, Hamilton E, Pohlmann PR, Tolaney SM, Chang CW, et al. Atezolizumab plus nab-paclitaxel in the treatment of metastatic triple-negative breast cancer with 2-year survival follow-up: a phase 1b clinical trial. JAMA Oncol. 2019;5(3):334–42.

    Article  PubMed  Google Scholar 

  97. Loi S, Giobbie-Hurder A, Gombos A, Bachelot T, Hui R, Curigliano G, et al. Pembrolizumab plus trastuzumab in trastuzumab-resistant, advanced, HER2-positive breast cancer (PANACEA): a single-arm, multicentre, phase 1b-2 trial. Lancet Oncol. 2019;20(3):371–82.

    Article  CAS  PubMed  Google Scholar 

  98. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N Engl J Med. 2018;379(22):2108–21.

    Article  CAS  PubMed  Google Scholar 

  99. Schmid P, Rugo HS, Adams S, Schneeweiss A, Barrios CH, Iwata H, et al. Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 2020;21(1):44–59.

    Article  CAS  PubMed  Google Scholar 

  100. Solinas C, Gombos A, Latifyan S, Piccart-Gebhart M, Kok M, Buisseret L. Targeting immune checkpoints in breast cancer: an update of early results. ESMO Open. 2017;2(5):e000255.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Emens LA. Immunotherapy in triple-negative breast cancer. Cancer J. 2021;27(1):59–66.

    Article  CAS  PubMed  Google Scholar 

  102. Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol. 2007;8(8):R157.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Rody A, Karn T, Liedtke C, Pusztai L, Ruckhaeberle E, Hanker L, et al. A clinically relevant gene signature in triple negative and basal-like breast cancer. Breast Cancer Res. 2011;13(5):R97.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Alexe G, Dalgin GS, Scanfeld D, Tamayo P, Mesirov JP, DeLisi C, et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res. 2007;67(22):10669–76.

    Article  CAS  PubMed  Google Scholar 

  105. Schmidt M, Bohm D, von Torne C, Steiner E, Puhl A, Pilch H, et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 2008;68(13):5405–13.

    Article  CAS  PubMed  Google Scholar 

  106. Schmidt M, Hellwig B, Hammad S, Othman A, Lohr M, Chen Z, et al. A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin kappa C as a compatible prognostic marker in human solid tumors. Clin Cancer Res. 2012;18(9):2695–703.

    Article  CAS  PubMed  Google Scholar 

  107. Hsu DS, Kim MK, Balakumaran BS, Acharya CR, Anders CK, Clay T, et al. Immune signatures predict prognosis in localized cancer. Cancer Investig. 2010;28(7):765–73.

    Article  CAS  Google Scholar 

  108. Bianchini G, Qi Y, Alvarez RH, Iwamoto T, Coutant C, Ibrahim NK, et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol. 2010;28(28):4316–23.

    Article  PubMed  Google Scholar 

  109. Nagalla S, Chou JW, Willingham MC, Ruiz J, Vaughn JP, Dubey P, et al. Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis. Genome Biol. 2013;14(4):R34.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Ascierto ML, Kmieciak M, Idowu MO, Manjili R, Zhao Y, Grimes M, et al. A signature of immune function genes associated with recurrence-free survival in breast cancer patients. Breast Cancer Res Treat. 2012;131(3):871–80.

    Article  CAS  PubMed  Google Scholar 

  111. Iglesia MD, Vincent BG, Parker JS, Hoadley KA, Carey LA, Perou CM, et al. Prognostic B-cell signatures using mRNA-seq in patients with subtype-specific breast and ovarian cancer. Clin Cancer Res. 2014;20(14):3818–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Coronella JA, Spier C, Welch M, Trevor KT, Stopeck AT, Villar H, et al. Antigen-driven oligoclonal expansion of tumor-infiltrating B cells in infiltrating ductal carcinoma of the breast. J Immunol. 2002;169(4):1829–36.

    Article  CAS  PubMed  Google Scholar 

  113. Hansen MH, Nielsen H, Ditzel HJ. The tumor-infiltrating B cell response in medullary breast cancer is oligoclonal and directed against the autoantigen actin exposed on the surface of apoptotic cancer cells. Proc Natl Acad Sci U S A. 2001;98(22):12659–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Nzula S, Going JJ, Stott DI. Antigen-driven clonal proliferation, somatic hypermutation, and selection of B lymphocytes infiltrating human ductal breast carcinomas. Cancer Res. 2003;63(12):3275–80.

    CAS  PubMed  Google Scholar 

  115. Perez EA, Thompson EA, Ballman KV, Anderson SK, Asmann YW, Kalari KR, et al. Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the north central cancer treatment group n9831 adjuvant trastuzumab trial. J Clin Oncol. 2015;33(7):701–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Rody A, Holtrich U, Pusztai L, Liedtke C, Gaetje R, Ruckhaeberle E, et al. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res. 2009;11(2):R15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  117. Akalay I, Janji B, Hasmim M, Noman MZ, Andre F, De Cremoux P, et al. Epithelial-to-mesenchymal transition and autophagy induction in breast carcinoma promote escape from T-cell-mediated lysis. Cancer Res. 2013;73(8):2418–27.

    Article  CAS  PubMed  Google Scholar 

  118. Akalay I, Tan TZ, Kumar P, Janji B, Mami-Chouaib F, Charpy C, et al. Targeting WNT1-inducible signaling pathway protein 2 alters human breast cancer cell susceptibility to specific lysis through regulation of KLF-4 and miR-7 expression. Oncogene. 2015;34(17):2261–71.

    Article  CAS  PubMed  Google Scholar 

  119. Kudo-Saito C, Shirako H, Takeuchi T, Kawakami Y. Cancer metastasis is accelerated through immunosuppression during Snail-induced EMT of cancer cells. Cancer Cell. 2009;15(3):195–206.

    Article  CAS  PubMed  Google Scholar 

  120. Ye X, Weinberg RA. Epithelial-mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 2015;25(11):675–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Yang B, Chou J, Tao Y, Wu D, Wu X, Li X, et al. An assessment of prognostic immunity markers in breast cancer. NPJ Breast Cancer. 2018;4:35.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Zhang H, Qin G, Yu H, Han X, Zhu S. Comprehensive genomic and immunophenotypic analysis of CD4 T cell infiltrating human triple-negative breast cancer. Cancer Immunol Immunother. 2021;70(6):1649–65.

    Article  CAS  PubMed  Google Scholar 

  123. Gruosso T, Gigoux M, Manem VSK, Bertos N, Zuo D, Perlitch I, et al. Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J Clin Invest. 2019;129(4):1785–800.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Wu Q, Li B, Li Z, Li J, Sun S, Sun S. Cancer-associated adipocytes: key players in breast cancer progression. J Hematol Oncol. 2019;12(1):95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. 2011;17(11):1498–503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Wang YY, Attane C, Milhas D, Dirat B, Dauvillier S, Guerard A, et al. Mammary adipocytes stimulate breast cancer invasion through metabolic remodeling of tumor cells. JCI Insight. 2017;2(4):e87489.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Wu Z, Wu J, Zhao Q, Fu S, Jin J. Emerging roles of aerobic glycolysis in breast cancer. Clin Transl Oncol. 2020;22(5):631–46.

    Article  CAS  PubMed  Google Scholar 

  128. Tang J, Luo Y, Wu G. A glycolysis-related gene expression signature in predicting recurrence of breast cancer. Aging (Albany NY). 2020;12(24):24983–94.

    Article  CAS  Google Scholar 

  129. Li C, Li X, Li G, Sun L, Zhang W, Jiang J, et al. Identification of a prognosisassociated signature associated with energy metabolism in triplenegative breast cancer. Oncol Rep. 2020;44(3):819–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Zhang D, Zheng Y, Yang S, Li Y, Wang M, Yao J, et al. Identification of a novel glycolysis-related gene signature for predicting breast cancer survival. Front Oncol. 2020;10:596087.

    Article  PubMed  Google Scholar 

  131. Sturtz LA, Deyarmin B, van Laar R, Yarina W, Shriver CD, Ellsworth RE. Gene expression differences in adipose tissue associated with breast tumorigenesis. Adipocytes. 2014;3(2):107–14.

    Article  CAS  Google Scholar 

  132. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95(25):14863–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Simon RMKE, McShane LM, Radmacher MD, Wright GW, Zhao Y. Statistics for biology and health: design and analysis of DNA microarray investigations. New York, NY: Springer; 2004.

    Google Scholar 

  134. Kato K. Algorithm for in vitro diagnostic multivariate index assay. Breast Cancer. 2009;16(4):248–51.

    Article  PubMed  Google Scholar 

  135. Deng JL, Xu YH, Wang G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet. 2019;10:695.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Tang D, Zhao X, Zhang L, Wang Z, Wang C. Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses. J Cell Biochem. 2019;120(6):9522–31.

    Article  CAS  PubMed  Google Scholar 

  137. Petersen K, Rajcevic U, Abdul Rahim SA, Jonassen I, Kalland KH, Jimenez CR, et al. Gene set based integrated data analysis reveals phenotypic differences in a brain cancer model. PLoS One. 2013;8(7):e68288.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11(4):417–22.

    Article  CAS  PubMed  Google Scholar 

  139. Jackson HW, Fischer JR, Zanotelli VRT, Ali HR, Mechera R, Soysal SD, et al. The single-cell pathology landscape of breast cancer. Nature. 2020;578(7796):615–20.

    Article  CAS  PubMed  Google Scholar 

  140. Wagner J, Rapsomaniki MA, Chevrier S, Anzeneder T, Langwieder C, Dykgers A, et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell. 2019;177(5):1330–45 e18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Schapiro D, Jackson HW, Raghuraman S, Fischer JR, Zanotelli VRT, Schulz D, et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods. 2017;14(9):873–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Schulz D, Zanotelli VRT, Fischer JR, Schapiro D, Engler S, Lun XK, et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 2018;6(1):25–36 e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Brady L, Kriner M, Coleman I, Morrissey C, Roudier M, True LD, et al. Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling. Nat Commun. 2021;12(1):1426.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Merritt CR, Ong GT, Church SE, Barker K, Danaher P, Geiss G, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol. 2020;38(5):586–99.

    Article  CAS  PubMed  Google Scholar 

  145. Lawson DA, Bhakta NR, Kessenbrock K, Prummel KD, Yu Y, Takai K, et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature. 2015;526(7571):131–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Reuter JA, Ortiz-Urda S, Kretz M, Garcia J, Scholl FA, Pasmooij AM, et al. Modeling inducible human tissue neoplasia identifies an extracellular matrix interaction network involved in cancer progression. Cancer Cell. 2009;15(6):477–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Hoshino A, Costa-Silva B, Shen TL, Rodrigues G, Hashimoto A, Tesic Mark M, et al. Tumour exosome integrins determine organotropic metastasis. Nature. 2015;527(7578):329–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Li B, Li T, Liu JS, Liu XS. Computational deconvolution of tumor-infiltrating immune components with bulk tumor gene expression data. Methods Mol Biol. 2020;2120:249–62.

    Article  CAS  PubMed  Google Scholar 

  149. Racle J, Gfeller D. EPIC: a tool to estimate the proportions of different cell types from bulk gene expression data. Methods Mol Biol. 2020;2120:233–48.

    Article  CAS  PubMed  Google Scholar 

  150. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Zhu L, Narloch JL, Onkar S, Joy M, Broadwater G, Luedke C, et al. Metastatic breast cancers have reduced immune cell recruitment but harbor increased macrophages relative to their matched primary tumors. J Immunother Cancer. 2019;7(1):265.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Clinical Medicine, Centre for Cancer Biomarkers CCBIO, University of Bergen, Bergen, Norway

    Elisabeth Wik & Lise M. Ingebriktsen

  2. Department of Pathology, Haukeland University Hospital, Bergen, Norway

    Elisabeth Wik

  3. Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Lars A. Akslen

Authors
  1. Elisabeth Wik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lise M. Ingebriktsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lars A. Akslen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elisabeth Wik .

Editor information

Editors and Affiliations

  1. Centre for Cancer Biomarkers CCBIO Department of Clinical Medicine, University of Bergen, Bergen, Norway

    Lars A. Akslen

  2. Department of Surgery, Harvard Medical School, Vascular Biology Program, Boston Children’s Hospital, Boston, USA

    Randolph S. Watnick

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wik, E., Ingebriktsen, L.M., Akslen, L.A. (2022). Gene Expression Signatures of the Tumor Microenvironment: Relation to Tumor Phenotypes and Progress in Breast Cancer. In: Akslen, L.A., Watnick, R.S. (eds) Biomarkers of the Tumor Microenvironment. Springer, Cham. https://doi.org/10.1007/978-3-030-98950-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98950-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98949-1

  • Online ISBN: 978-3-030-98950-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation