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Breast Cancer Research
Published in: Breast Cancer Research 1/2019

Open Access 01-12-2019 | Metastasis | Research article

Separation of breast cancer and organ microenvironment transcriptomes in metastases

Authors: Mohammad A. Alzubi, Tia H. Turner, Amy L. Olex, Sahib S. Sohal, Nicholas P. Tobin, Susana G. Recio, Jonas Bergh, Thomas Hatschek, Joel S. Parker, Carol A. Sartorius, Charles M. Perou, Mikhail G. Dozmorov, J. Chuck Harrell

Published in: Breast Cancer Research | Issue 1/2019

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Abstract

Background

The seed and soil hypothesis was proposed over a century ago to describe why cancer cells (seeds) grow in certain organs (soil). Since then, the genetic properties that define the cancer cells have been heavily investigated; however, genomic mediators within the organ microenvironment that mediate successful metastatic growth are less understood. These studies sought to identify cancer- and organ-specific genomic programs that mediate metastasis.

Methods

In these studies, a set of 14 human breast cancer patient-derived xenograft (PDX) metastasis models was developed and then tested for metastatic tropism with two approaches: spontaneous metastases from mammary tumors and intravenous injection of PDX cells. The transcriptomes of the cancer cells when growing as tumors or metastases were separated from the transcriptomes of the microenvironment via species-specific separation of the genomes. Drug treatment of PDX spheroids was performed to determine if genes activated in metastases may identify targetable mediators of viability.

Results

The experimental approaches that generated metastases in PDX models were identified. RNA sequencing of 134 tumors, metastases, and normal non-metastatic organs identified cancer- and organ-specific genomic properties that mediated metastasis. A common genomic response of the liver microenvironment was found to occur in reaction to the invading PDX cells. Genes within the cancer cells were found to be either transiently regulated by the microenvironment or permanently altered due to clonal selection of metastatic sublines. Gene Set Enrichment Analyses identified more than 400 gene signatures that were commonly activated in metastases across basal-like PDXs. A Src signaling signature was found to be extensively upregulated in metastases, and Src inhibitors were found to be cytotoxic to PDX spheroids.

Conclusions

These studies identified that during the growth of breast cancer metastases, there were genomic changes that occurred within both the cancer cells and the organ microenvironment. We hypothesize that pathways upregulated in metastases are mediators of viability and that simultaneously targeting changes within different cancer cell pathways and/or different tissue compartments may be needed for inhibition of disease progression.
Appendix
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Literature
1.
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.CrossRefPubMed
2.
Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.PubMedPubMedCentralCrossRef
3.
Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan M, Yau C, Kandoth C, et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell. 2015;163(2):506–19.PubMedPubMedCentralCrossRef
4.
Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.CrossRef
5.
Harrell JC, Prat A, Parker JS, Fan C, He X, Carey L, Anders C, Ewend M, Perou CM. Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse. Breast Cancer Res Treat. 2012;132(2):523–35.PubMedCrossRef
6.
Kennecke H, Yerushalmi R, Woods R, Cheang MC, Voduc D, Speers CH, Nielsen TO, Gelmon K. Metastatic behavior of breast cancer subtypes. J Clin Oncol. 2010;28(20):3271–7.PubMedCrossRef
7.
Hosseini H, Obradovic MM, Hoffmann M, Harper KL, Sosa MS, Werner-Klein M, Nanduri LK, Werno C, Ehrl C, Maneck M, et al. Early dissemination seeds metastasis in breast cancer. Nature. 2016;540:552–8.
8.
Harrell JC, Pfefferle AD, Zalles N, Prat A, Fan C, Khramtsov A, Olopade OI, Troester MA, Dudley AC, Perou CM. Endothelial-like properties of claudin-low breast cancer cells promote tumor vascular permeability and metastasis. Clin Exp Metastasis. 2014;31(1):33–45.PubMedCrossRef
9.
Knott SRV, Wagenblast E, Khan S, Kim SY, Soto M, Wagner M, Turgeon MO, Fish L, Erard N, Gable AL, et al. Asparagine bioavailability governs metastasis in a model of breast cancer. Nature. 2018;554(7692):378–81.PubMedPubMedCentralCrossRef
10.
Wagenblast E, Soto M, Gutierrez-Angel S, Hartl CA, Gable AL, Maceli AR, Erard N, Williams AM, Kim SY, Dickopf S, et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature. 2015;520(7547):358–62.PubMedPubMedCentralCrossRef
11.
Fidler IJ. Metastasis: quantitative analysis of distribution and fate of tumor emboli labeled with 125 I-5-iodo-2′-deoxyuridine. J Natl Cancer Inst. 1970;45(4):773–82.PubMed
12.
Harrell JC, Dye WW, Harvell DM, Sartorius CA, Horwitz KB. Contaminating cells alter gene signatures in whole organ versus laser capture microdissected tumors: a comparison of experimental breast cancers and their lymph node metastases. Clin Exp Metastasis. 2008;25(1):81–8.PubMedCrossRef
13.
Camp JT, Elloumi F, Roman-Perez E, Rein J, Stewart DA, Harrell JC, Perou CM, Troester MA. Interactions with fibroblasts are distinct in basal-like and luminal breast cancers. Mol Cancer Res. 2011;9(1):3–13.PubMedCrossRef
14.
Park ES, Kim SJ, Kim SW, Yoon SL, Leem SH, Kim SB, Kim SM, Park YY, Cheong JH, Woo HG, et al. Cross-species hybridization of microarrays for studying tumor transcriptome of brain metastasis. Proc Natl Acad Sci U S A. 2011;108(42):17456–61.PubMedPubMedCentralCrossRef
15.
Iorns E, Clarke J, Ward T, Dean S, Lippman M. Simultaneous analysis of tumor and stromal gene expression profiles from xenograft models. Breast Cancer Res Treat. 2012;131(1):321–4.PubMedCrossRef
16.
DeRose YS, Gligorich KM, Wang G, Georgelas A, Bowman P, Courdy SJ, Welm AL, Welm BE: Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine. Curr Protoc Pharmacol 2013, Chapter 14:Unit14 23.
17.
Turner TH, Alzubi MA, Sohal SS, Olex AL, Dozmorov MG, Harrell JC. Characterizing the efficacy of cancer therapeutics in patient-derived xenograft models of metastatic breast cancer. Breast Cancer Res Treat. 2018;170(2):221–34.PubMedCrossRef
18.
Callari M, Batra AS, Batra RN, Sammut SJ, Greenwood W, Clifford H, Hercus C, Chin SF, Bruna A, Rueda OM, et al. Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts. BMC Genomics. 2018;19(1):19.PubMedPubMedCentralCrossRef
19.
20.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.PubMedCrossRef
21.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.PubMedPubMedCentralCrossRef
22.
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14(4):417–9.PubMedPubMedCentralCrossRef
23.
Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 2015;4:1521.PubMedCrossRef
24.
Gendoo DM, Ratanasirigulchai N, Schroder MS, Pare L, Parker JS, Prat A, Haibe-Kains B. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics. 2016;32(7):1097–9.PubMedCrossRef
25.
Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71.PubMedCrossRef
26.
27.
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9.PubMedCrossRef
28.
29.
Slowikowski K, Schep A, Hughes S, Lukauskas S, Irisson J-O, N Kamvar Z, Ryan T, Christophe D, Hiroaki Y, Gramme P: ggrepel: automatically position non-overlapping text labels with ‘ggplot2’. 2018.
30.
Siegel MB, He X, Hoadley KA, Hoyle A, Pearce JB, Garrett AL, Kumar S, Moylan VJ, Brady CM, Van Swearingen AE, et al. Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer. J Clin Invest. 2018;128(4):1371–83.PubMedPubMedCentralCrossRef
31.
Tobin NP, Harrell JC, Lovrot J, Egyhazi Brage S, Frostvik Stolt M, Carlsson L, Einbeigi Z, Linderholm B, Loman N, Malmberg M, et al. Molecular subtype and tumor characteristics of breast cancer metastases as assessed by gene expression significantly influence patient post-relapse survival. Ann Oncol. 2015;26(1):81–8.PubMedCrossRef
32.
Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.
33.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, 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.PubMedPubMedCentralCrossRef
34.
Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417–25.
35.
de Hoon MJ, Imoto S, Nolan J, Miyano S. Open source clustering software. Bioinformatics. 2004;20(9):1453–4.PubMedCrossRef
36.
Saldanha AJ. Java Treeview--extensible visualization of microarray data. Bioinformatics. 2004;20(17):3246–8.PubMedCrossRef
37.
Bos PD, Zhang XH, Nadal C, Shu W, Gomis RR, Nguyen DX, Minn AJ, van de Vijver MJ, Gerald WL, Foekens JA, et al. Genes that mediate breast cancer metastasis to the brain. Nature. 2009;459(7249):1005–9.PubMedPubMedCentralCrossRef
38.
Puchalapalli M, Zeng X, Mu L, Anderson A, Hix Glickman L, Zhang M, Sayyad MR, Mosticone Wangensteen S, Clevenger CV, Koblinski JE. NSG mice provide a better spontaneous model of breast cancer metastasis than athymic (nude) mice. PLoS One. 2016;11(9):e0163521.PubMedPubMedCentralCrossRef
39.
Sartorius CA, Hanna CT, Gril B, Cruz H, Serkova NJ, Huber KM, Kabos P, Schedin TB, Borges VF, Steeg PS, et al. Estrogen promotes the brain metastatic colonization of triple negative breast cancer cells via an astrocyte-mediated paracrine mechanism. Oncogene. 2016;35(22):2881–92.PubMedCrossRef
40.
Paez-Ribes M, Man S, Xu P, Kerbel RS. Development of patient derived xenograft models of overt spontaneous breast cancer metastasis: a cautionary note. PLoS One. 2016;11(6):e0158034.PubMedPubMedCentralCrossRef
41.
Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.PubMedPubMedCentralCrossRef
42.
DeRose YS, Wang G, Lin YC, Bernard PS, Buys SS, Ebbert MT, Factor R, Matsen C, Milash BA, Nelson E, et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat Med. 2011;17(11):1514–20.PubMedPubMedCentralCrossRef
43.
Sachs N, de Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H, et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell. 2018;172(1–2):373–86 e310.PubMedCrossRef
44.
45.
Gautschi O, Tepper CG, Purnell PR, Izumiya Y, Evans CP, Green TP, Desprez PY, Lara PN, Gandara DR, Mack PC, et al. Regulation of Id1 expression by SRC: implications for targeting of the bone morphogenetic protein pathway in cancer. Cancer Res. 2008;68(7):2250–8.PubMedCrossRef
46.
Gucalp A, Sparano JA, Caravelli J, Santamauro J, Patil S, Abbruzzi A, Pellegrino C, Bromberg J, Dang C, Theodoulou M, et al. Phase II trial of saracatinib (AZD0530), an oral SRC-inhibitor for the treatment of patients with hormone receptor-negative metastatic breast cancer. Clin Breast Cancer. 2011;11(5):306–11.
47.
Finn RS, Bengala C, Ibrahim N, Roche H, Sparano J, Strauss LC, Fairchild J, Sy O, Goldstein LJ. Dasatinib as a single agent in triple-negative breast cancer: results of an open-label phase 2 study. Clin Cancer Res. 2011;17(21):6905–13.
48.
Lawson DA, Bhakta NR, Kessenbrock K, Prummel KD, Yu Y, Takai K, Zhou A, Eyob H, Balakrishnan S, Wang CY, et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature. 2015;526(7571):131–5.PubMedPubMedCentralCrossRef
49.
Stupnikov A, O'Reilly PG, McInerney CE, Roddy AC, Dunne PD, Gilmore A, Ellis HP, Flannery T, Healy E, McIntosh SA, et al. Impact of variable RNA-sequencing depth on gene expression signatures and target compound robustness: case study examining brain tumor (glioma) disease progression. JCO Precis Oncol. 2. https://​www.​ncbi.​nlm.​nih.​gov/​pubmed/​30324181.
50.
Metadata
Title
Separation of breast cancer and organ microenvironment transcriptomes in metastases
Authors
Mohammad A. Alzubi
Tia H. Turner
Amy L. Olex
Sahib S. Sohal
Nicholas P. Tobin
Susana G. Recio
Jonas Bergh
Thomas Hatschek
Joel S. Parker
Carol A. Sartorius
Charles M. Perou
Mikhail G. Dozmorov
J. Chuck Harrell
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2019
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-019-1123-2

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