References
- Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–7 (2012).
- Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in Cancer Cells. Nature 483, 570–5 (2012).
- Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
- Behan, F. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
- Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).
- Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–60 (2015).
- Malladi, S. et al. Metastatic Latency and Immune Evasion through Autocrine Inhibition of WNT. Cell 165, 45–60 (2016).
- van der Weyden, L. et al. Genome-wide in vivo screen identifies novel host regulators of metastatic colonization. Nature 541, 233–236 (2017).
- Tasdogan, A. et al. Metabolic heterogeneity confers differences in melanoma metastatic potential. Nature 577, 115–120 (2019).
- Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).
- Kennecke, H. et al. Metastatic Behavior of Breast Cancer Subtypes. J Clin Oncol 28, 3271–3277 (2010).
- Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat Biotechnol 34, 419–423 (2016).
- Budczies, J. et al. The landscape of metastatic progression patterns across major human cancers. Oncotarget 6, 570–583 (2014).
- Müller, C. et al. Hematogenous dissemination of glioblastoma multiforme. Sci Transl Med 6, 247ra101 (2014).
- Fonkem, E., Lun, M. & Wong, E. T. Rare Phenomenon of Extracranial Metastasis of Glioblastoma. J Clin Oncol 29, 4594–4595 (2011).
- Stone, K. R., Mickey, D. D., Wunderli, H., Mickey, G. H. & Paulson, D. F. Isolation of a human prostate carcinoma cell line (DU 145). Int J Cancer 21, 274–281 (1978).
- 1Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. A molecular sigNature of metastasis in primary solid tumors. Nat Genet 33, 49–54 (2002).
- Zhang, X. H.-F. et al. Selection of bone metastasis seeds by mesenchymal signals in the primary tumor stroma. Cell 154, 1060–73 (2013).
- Campbell, P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–13 (2010).
- Witzel, I., Oliveira-Ferrer, L., Pantel, K., Müller, V. & Wikman, H. Breast cancer brain metastases: biology and new clinical perspectives. Breast Cancer Res Bcr 18, 8 (2016).
- Valiente, M. et al. The Evolving Landscape of Brain Metastasis. Trends Cancer 4, 176–196 (2018).
- Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–52 (2012).
- Koboldt, D. C. et al. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
- Razavi, P. et al. The Genomic Landscape of Endocrine-Resistant Advanced Breast Cancers. Cancer Cell 34, 427-438.e6 (2018).
- Gatza, M. L. et al. A pathway-based classification of human breast cancer. P Natl Acad Sci Usa 107, 6994–9 (2010).
- Creighton, C. J. et al. Proteomic and transcriptomic profiling reveals a link between the PI3K pathway and lower estrogen-receptor (ER) levels and activity in ER+ breast cancer. Breast Cancer Res Bcr 12, R40 (2010).
- Ricoult, S. J. H., Yecies, J. L., Ben-Sahra, I. & Manning, B. D. Oncogenic PI3K and K-Ras stimulate de novo lipid synthesis through mTORC1 and SREBP. Oncogene 35, 1250–60 (2015).
- Cai, Y. et al. Loss of Chromosome 8p Governs Tumor Progression and Drug Response by Altering Lipid Metabolism. Cancer Cell 29, 751–66 (2016).
- Li, H. et al. The landscape of Cancer Cell line metabolism. Nat Med 25, 850–860 (2019).
- Patra, K. C. & Hay, N. The pentose phosphate pathway and cancer. Trends Biochem Sci 39, 347–54 (2014).
- Koundouros, N. & Poulogiannis, G. Reprogramming of fatty acid metabolism in cancer. Brit J Cancer 122, 4–22 (2019).
- ain, M. et al. A systematic survey of lipids across mouse tissues. Am J Physiology Endocrinol Metabolism 306, E854-68 (2014).
- Paget, S. The distribution of secondary growths in cancer of the breast. 1889. Cancer Metastasis Rev 8, 98–101 (1989).
- Dempster, J. et al. Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets. Nat Commun 10, 5817 (2019).
- Horton, J. D., Goldstein, J. L. & Brown, M. S. SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver. J Clin Invest 109, 1125–1131 (2002).
- Varešlija, D. et al. Transcriptome Characterization of Matched Primary Breast and Brain Metastatic Tumors to Detect Novel Actionable Targets. Jnci J National Cancer Inst 111, 388–398 (2018).
- Angelova, M. et al. Evolution of Metastases in Space and Time under Immune Selection. Cell 175, 751-765.e16 (2018).
- Zhang, M. et al. Adipocyte-derived lipids mediate melanoma progression via FATP proteins. Cancer Discov 8, 1006–1025 (2018).
- Zou, Y. et al. Polyunsaturated Fatty Acids from Astrocytes Activate PPAR Gamma Signaling in Cancer Cells to Promote Brain Metastasis. Cancer Discov 9, 1720–1735 (2019).
- Pascual, G. et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541, 41–45 (2016).
- Zhang, C., Lowery, F. J. & Yu, D. Intracarotid Cancer Cell Injection to Produce Mouse Models of Brain Metastasis. J Vis Exp (2017) doi:10.3791/55085.
- Ozawa, T. & James, C. D. Establishing intracranial brain tumor xenografts with subsequent analysis of tumor growth and response to therapy using bioluminescence imaging. J Vis Exp Jove 1986 (2010) doi:10.3791/1986.
- Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Sci New York N Y 352, 189–96 (2016).
- Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–9 (2012).
- Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47 (2015).
- Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. Bmc Bioinformatics 12, 323 (2011).
- Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinform Oxf Engl 26, 139–40 (2009). /li>
- Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc National Acad Sci 102, 15545–15550 (2005).
- Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. Bmc Bioinformatics 14, 7 (2013).
- Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401–4 (2012).