Single-cell analysis tools for drug discovery and development

Nature Reviews Drug Discovery

Volumn 15, Issue 3, 2016, Pages 204-216

  Heath, James R ^a   Ribas, Antoni ^b   Mischel, Paul S ^c  

^a California Institute of Technology   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER IMMUNOTHERAPY; DNA SEQUENCE; DRUG DEVELOPMENT; GENE EXPRESSION; GENE MUTATION; GENETIC VARIABILITY; GENOMICS; HIGH THROUGHPUT SCREENING; HUMAN; METABOLOMICS; MULTIPLEX SINGLE CELL PROTEOMICS; NONHUMAN; PRIORITY JOURNAL; PROTEOMICS; REVIEW; SINGLE CELL ANALYSIS; TRANSCRIPTOMICS; TUMOR MICROENVIRONMENT; ANIMAL; PROCEDURES; TRENDS;

ANIMALS; DRUG DISCOVERY; GENOMICS; HUMANS; PROTEOMICS; SINGLE-CELL ANALYSIS;

EID: 84959369430     PISSN: 14741776     EISSN: 14741784     Source Type: Journal    
DOI: 10.1038/nrd.2015.16     Document Type: Review

References (166)

1. Sakmann, B., & Neher, E. Patch clamp techniques for studying ionic channels in excitable membranes. Annu. Rev. Physiol. 46, 455-472 (1984
2. Amann, R., & Fuch, B. M. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol. 6, 339-348 (2008
3. Langer-Safer, P. R., Levine, M., & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl Acad. Sci. USA 79, 4381-4385 (1982
4. Herzenberg, L. A., et al. The history of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 48, 1819-1827 (2002
5. Herzenberg, L. A., Julius, M. H., & Masuda, T. Demonstration that antigen-binding cells are precursors of antibody-producing cells after purification with a fluorescence-Activated cell sorter. Proc. Natl Acad. Sci. USA 69, 1934-1938 (1972
6. Czerkinsky, C., Nilsson, L., Nygren, H., Ouchterlony, O., & Tarkowski, A. A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of specific antibody-secreting cells. Immunol. Methods 65, 109-121 (1983
7. Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648-655 (2004)
8. Lu, Y., et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc. Natl Acad. Sci. USA 112, E607-E615 (2015
9. Bendall, S. C., et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687-696 (2011
10. Shi, Q., et al. Single cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419-425 (2012)
11. Irish, J. M., et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217-228 (2004
12. Navin, N., et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90-94 (2011
13. de Bourcy, C. F., et al. A quantitative compariso of single-cell whole genome amplification methods. PLoS ONE 9, e105585 (2014
14. Wang, J., Fan, H. C., Behr, B., & Quake, S. R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402-412 (2012
15. Tay, S., et al. Single-cell NF-?B dynamics reveal digital activation and analogue information processing. Nature 466, 267-271 (2010
16. Treutlein, B., et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375 (2014
17. Patel, A. P., et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1400 (2014
18. Fan, H. C., Fu, G. K., & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 6222 (2015)
19. Han, L., et al. Co detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4, 6485 (2014
20. Mazumder, A., Tummler, K., Bathe, M., & Samson, L. D. Single-cell analysis of ribonucleotide reductase transcriptional and translational response to DNA damage. Mol. Cell. Biol. 33, 635-642 (2013
21. Porichis, F., et al. High-Throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014
22. Stahlberg, A., Thomsen, C., Ruff, D., & Aman, P. Quantitative PCR analysis of DNA, RNAs and proteins in the same single cell. Clin. Chem. 58, 1682-1691 (2012
23. Xue, M., et al. Chemical methods for the simultaneous quantitation of metabolites and proteins from single cells. J. Am. Chem. Soc. 137, 4066-4069 (2015
24. Wang, J., et al Quantitating cell-cell interaction functions, with applications to glioblastoma multiforme cancer cells. Nano Lett. 12, 6101-6106 (2012
25. Liadi, I., et al. Individual motile CD4+ T cells can participate in efficient multikilling through conjugation to multiple tumor cells. Cancer Immunol. Res. 3, 473-482 (2015
26. Elitas, M., Brower, K., Lu, Y., Chen, J. J., & Fan, R. A microchip platform for interrogating tumormacrophage paracrine signaling at the single-cell level. Lab Chip 14, 3582-3588 (2014
27. Wei, W., et al. Hypoxia induces a phase transition within a kinase signaling network in cancer cells. Proc. Natl Acad. Sci. USA 110, e1352-e1360 (2013
28. Mehling, M., Frank, T., Albayrak, C., & Tay, S. Real-Time tracking, retrieval and gene expression analysis of migrating human T cells. Lab Chip 15, 1276-1283 (2015
29. Brouzes, E., et al. Droplet microfluidic technology for single-cell high-Throughput screening. Proc. Natl Acad. Sci. USA 106, 14195-14200 (2009
30. Sendra, V. G., Lie, A., Romain, G., Agarwal, S. K., & Varadarajan, N. Detection and isolation of auto-reactive human antibodies from primary B cells. Methods 64, 153-159 (2013)
31. Love, J. C., Ronan, J. L., Grotenbreg, G. M., Van der Veen, A. G., & Ploegh, H. L. A microengraving method for rapid selection of single cells producing antigen specific antibodies. Nat. Biotechnol. 24, 703-707 (2006
32. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M., & Long, C. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360-361 (2014
33. Yang, B., et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945-958 (2014
34. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, 6233 (2015)
35. Angelo, M., et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436-442 (2014
36. Shin, Y. S., et al. Chemistries for patterning robust DNA microbarcodes enable multiplex assays of cytoplasm proteins from single cancer cells. ChemPhysChem 11, 3063 (2010
37. Amir, E. D., et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545-548 (2013
38. Shin, Y. S., et al. Protein signaling networks from single cell fluctuations and information theory profiling. Biophys. J. 100, 2378-2386 (2011
39. Jaitin, D. A., et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776-779 (2014
40. Kravchenko-Balasha, N., Wang, J., Remacle, F., Levine, R. D., & Heath, J. R. Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions. Proc. Natl Acad. Sci. USA 111, 6521-6526 (2014
41. Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J., & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770-E2777 (2014
42. Shapiro, E., Biezuner, T., & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618-630 (2013
43. Blainey, P. C., & Quake, S. R. Dissecting genomic diversity, one cell at a time. Nat. Methods 11, 19-21 (2014
44. Zhang, L., et al. Whole genome amplification from a single cell: implications for genetic analysis. Proc. Natl Acad. Sci. USA 89, 5847-5851 (1992
45. Acinas, S. G., Sarma-Rupavtarm, R., Klepac-Ceraj, V., & Polz, M. F. PCR-induced sequence artifacts and bias: insights from comparison of two 16S rRNA clone libraries constructed from the same sample. Appl. Environ. Microbiol. 71, 8966-8969 (2005)
46. Dean, F. B., Nelson, J. R., Giesler, T. L., & Lasken, R. S. Rapid amplification of plasmid and phage DNA using Phi29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11, 1095-1099 (2001
47. Dago, A. E., et al. Rapid phenotypic and genomic change in response to therapeutic pressure in prostate cancer inferred by high content analysis of single circulating tumor cells. PLoS ONE 9, e101777 (2014
48. Zong, C., Lu, S., Chapman, A. R., & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622-1626 (2012)
49. Ni, X., et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc. Natl Acad. Sci. USA 110, 21083-21088 (2013
50. Francis, J. M., et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. Cancer Discov. 4, 956-971 (2014
51. Dash, P., et al. Paired analysis of TCRα and TCRβ chains at the single-cell level in mice. J. Clin. Invest. 121, 288-295 (2011
52. Kim, S. M., et al. Analysis of the paired TCR α-And β chains of single human T cells. PLoS ONE 7, e37338 (2013
53. Han, A., Glanville, J., Hansmann, L., & Davis, M. M. Linking T cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684-692 (2014)
54. Ng, S. B., et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272-276 (2009
55. Mamanova, L., et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111-118 (2010
56. Hodges, E., et al. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39, 1522-1527 (2007
57. Hou, Y., et al. Single-cell exome sequencing and monoclonal evolution of a JAK2 negative myeloproliferative neoplasm. Cell 148, 873-885 (2012
58. Xu, X., et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886-895 (2012
59. Lohr, J. G., et al. Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Nat. Biotechnol. 32, 479-484 (2014
60. Eberwine, J., et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010-3014 (1992
61. Tang, F., et al. mRNA-seq whole-Transcriptome analysis of a single cell. Nat. Methods 6, 377-382 (2009
62. Islam, S., et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163-166 (2014
63. Wu, A. R., et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41-46 (2014
64. Shalek, A. K., et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236-240 (2013
65. Pollen, A. A., et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053-1058 (2014
66. Ting, D. T., et al. Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 8, 1905-1918 (2014
67. Luo, Y., et al. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells. Cell 161, 1175-1186 (2015
68. Klein, A. M., et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015)
69. Macosko, E. Z., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015
70. Kivioja, T., et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72-74 (2012)
71. Grun, D., Kester, L., & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637-640 (2014)
72. Polz, M. F., & Cavanaugh, C. M. Bias in template-To product ratios in multitemplate PCR. Appl. Environ. Microbiol. 64, 3724-3730 (1998
73. Schloss, P. D., Gevers, D., & Westcott, S. L. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE 6, e27310 (2011
74. Suzuki, M. T., & Giovannoni, S. J. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl. Environ. Microbiol. 62, 625-630 (1996
75. Brennecke, P., et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093-1095 (2013
76. Fu, G. K., Wilhelmy, J., Stern, D., Fan, H. C., & Fodor, S. P. A. Digital encoding of cellular mRNAs enabling precise and absolute gene expression measurement by single-molecule counting. Anal. Chem. 86, 2867-2870 (2014
77. Fu, G. K., et al. Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations. Proc. Natl Acad. Sci. USA 111, 1891-1896 (2014
78. Picelli, S., et al. Full-length RNA-seq from single cells using Smart seq2. Nat. Protoc. 9, 171-181 (2014
79. Yates, J. R., Ruse, C. I., & Nakorchevsky, A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11, 49-79 (2009
80. Picotti, P., & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9, 555-566 (2012
81. Torres, A. J., Contento, R. L., Gordo, S., Wucherpfennig, K. W., & Love, C. L. Functional single-cell analysis of T cell activation by supported bilayer-Tethered ligands on arrays of nanowells. Lab Chip 13, 90-99 (2013
82. Hughes, A. J., et al. Single-cell western blotting. Nat. Methods 11, 749-755 (2014
83. Guo, M. T., Rotem, A., Heyman, J. A., & Weitz, D. A. Droplet microfluidics for high-Throughput biological assays. Lab Chip 12, 2146-2155 (2012
84. Huebner, A., et al. Static microdroplet arrays: a microfluidic device for droplet trapping, incubation and release for enzymatic and cell-based assays. Lab Chip 9, 692-698 (2009
85. Kintses, B., van Vliet, L. D., Devenish, S. R. A., & Hollfelder, F. Microfluidic droplets: new integrated workflows for biological experiments. Curr. Opin. Chem. Biol. 14, 548-555 (2010
86. Mazutis, L., et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870-891 (2013
87. Yu, J., et al. Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications. Annu. Rev. Anal. Chem. 7, 275-295 (2014)
88. McKelvey-Martin, V. J., et al. The single cell gel electrophoresis assay (comet assay): a European review. Mut. Res. 288, 47-63 (1993
89. Weingeist, D. M., et al. Single-cell microarray enables high-Throughput evaluation of DNA double-strand breaks and DNA repair inhibitors. Cell Cycle 12, 907-915 (2013
90. Han, Q., et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl Acad. Sci. USA 109, 1607-1612 (2012
91. Lu, Y., et al. High-Throughput secretomic analysis of single cells to assess functional cellular heterogeneity. Anal. Chem. 85, 2548-2556 (2013
92. Romain, G., et al. Antibody Fc engineering improves frequency and promotes kinetic boosting of serial killing mediated by NK cells. Blood 124, 3241-3249 (2014
93. Marcon, E., et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat. Methods 12, 725-731 (2015)
94. Towbin, H., Staehelin, T., & Gordon, J. Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc. Natl Acad. Sci. USA 76, 4350-4354 (1979
95. Ma, C., et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17, 738-744 (2011
96. Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183-1186 (2002)
97. Kaern, M., Elston, T. C., Blake, W. J., & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451-464 (2005
98. Bengtsson, M., Stahlberg, A., Rorsman, P., & Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388-1392 (2005
99. Ma, C., et al. Multifunctional T cell analyses to study response and progression in adoptive cell transfer immunotherapy. Cancer Discov. 3, 418-429 (2013)
100. Powell, A. A., et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7, e33788 (2012
101. Halo, T. L., et al. NanoFlares for the detection, isolation, and culture of live tumor cells from human blood. Proc. Natl Acad. Sci. USA 111, 17104-17109 (2014
102. Dalerba, P., et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120-1127 (2011)
103. Zhao, J. L., et al. Conversion of danger signals into cytokine signals by hematopoietic stem and progenitor cells for regulation of stress-induced hematopoiesis. Cell Stem Cell 14, 1-15 (2014
104. Lin, L., et al. Human natural killer cells licensed by killer Ig receptor genes have an altered cytokine program that modifies CD4+ T cell function. J. Immunol. 193, 940-949 (2014
105. Van Buuren, M. M., Calis, J. J., & Schumacher, T. N. High sensitivity of cancer exome-based CD8 T cell neo-Antigen identification. Oncoimmunology 3, e28836 (2014
106. Kellogg, R. A., Géz-Sjérg, R., Leyrat, A. A., & Tay, S. High-Throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat. Protoc. 9, 1713-1726 (2014
107. Kellogg, R. A., & Tay, S. Noise facilitates transcriptional control under dynamic inputs. Cell 160, 381-392 (2015
108. Yamanaka, Y. J., et al. Single-cell analysis of the dynamics and functional outcomes of interactions between human natural killer cells and target cells. Integr. Biol. 4, 1175-1184 (2012
109. Gupta, P. B., et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633-644 (2011
110. Nathanson, D., et al. Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science 343, 72-76 (2014)
111. Jaiswal, S., & Weissman, I. L. Hematopoietic stem and progenitor cells and the inflammatory response. Ann. NY Acad. Sci. 1174, 118-121 (2009
112. Baldridge, M. T., King, K. Y., Boles, N. C., Weksberg, D. C., & Goodell, M. A. Quiescent haematopoietic stem cells are activated by IFN-γ in response to chronic infection. Nature 465, 793-797 (2010
113. Baldridge, M. T., King, K. Y., & Goodell, M. A. Inflammatory signals regulate hematopoietic stem cells. Trends Immunol. 32, 57-65 (2011
114. King, K. Y., & Goodell, M. A. Inflammatory modulation of HSCs: viewing the HSC as a foundation for the immune response. Nat. Rev. Immunol. 11, 685-692 (2011
115. Nagai, Y., et al. Toll-like receptors on hematopoietic progenitor cells stimulate innate immune system replenishment. Immunity 24, 801-812 (2006
116. Bodenmiller, B., et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858-867 (2012)
117. Couzin-Frankel, J. Cancer immunotherapy. Science 343, 1432 (2013
118. Ledford, H. Cancer treatment: the killer within. Nature 508, 24-26 (2014
119. Palucka, K., & Banchereau, J. Cancer immunotherapy via dendritic cells. Nat. Rev. Cancer 12, 265-277 (2012
120. Rosenberg, S. A., Restifo, N. P., Yang, J. C., Morgan, R. A., & Dudley, M. E. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat. Rev. Cancer 8, 299-308 (2008
121. Leach, D. R., Krummel, M. F., & Allison, J. P. Enhancement of antitumor immunity by CTLA 4 blockade. Science 271, 1734-1736 (1996
122. Ribas, A., & Tumeh, P. C. The future of cancer therapy: selecting patients who respond to PD 1/L1 blockade. Clin. Cancer Res. 20, 4982-4984 (2014
123. Iwai, Y., et al. Involvement of PD L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD L1 blockade. Proc. Natl Acad. Sci. USA 99, 12293-12297 (2002
124. Crompton, J. G., Sukumar, M., & Restifo, N. P. Uncoupling T cell expansion from effector differentiation in cell-based immunotherapy. Immunol. Rev. 257, 264-276 (2014
125. Segal, N. H., et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 68, 889-892 (2008
126. Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P., & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135-146 (2014
127. DuPage, M., Mazumdar, C., Schmidt, L. M., Cheung, A. F., & Jacks, T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405-409 (2012
128. Matsushita, H., et al. Cancer exome analysis reveals a T cell-dependent mechanism of cancer immunoediting. Nature 482, 400-404 (2012
129. Robbins, P. F., et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747-752 (2013
130. van Rooij, N., et al. Tumor exome analysis reveals neoantigen-specific T cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439-e442 (2013
131. De Rosa, S. C., Herzenberg, L. A., Herzeberg, L. A., & Roederer, M. 11 color, 13 parameter flow cytometry: identification of human naive T cells by phenotype, function, and T cell receptor diversity. Nat. Med. 7, 245-248 (2001
132. Lee, P. P., et al. Characterization of circulating T cells specific for tumor-Associated antigens in melanoma patients. Nat. Med. 5, 677-685 (1999
133. Altman, J. D., et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94-96 (1996
134. Ramachandiran, V., et al. A robust method for production of MHC tetramers with small molecule fluorophores. J. Immunol. Methods 319, 13-20 (2007
135. Bakker, A. H., et al. Conditional MHC class I ligands and peptide exchange technology for the human MHC gene products HLA A1,-A3,-A11, and-B7. Proc. Natl Acad. Sci. USA 105, 3825-3830 (2008
136. Celie, P. H., et al. UV induced ligand exchange in MHC class I protein crystals. J. Am. Chem. Soc. 131, 12298 (2009
137. Kwong, G. A., et al. Modular nucleic acid assembled p/MHC microarrays for multiplexed sorting of antigen-specific lymphophocytes. J. Am. Chem. Soc. 131, 9695-9703 (2009
138. Rodenko, B., et al. Generation of peptide-MHC class I complexes through UV mediated ligand exchange. Nat. Protoc. 1, 1120-1132 (2006
139. Bordon, Y. Immunotherapy: checkpoint parley. Nat. Rev. Cancer 15, 3 (2015
140. Schumacher, T. N., & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69-74 (2015
141. Rosenberg, S. A., & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62-68 (2015
142. Gattinoni, L., Klebanoff, C. A., & Restifo, N. P. Paths to stemness: building the ultimate antitumour T cell. Nat. Rev. Cancer 12, 671-684 (2012
143. Stroncek, D. F., et al. New directions in cellular therapy of cancer: a summary of the summit on cellular therapy for cancer. J. Transl. Med. 10, 48-52 (2012
144. Restifo, N. P., & Gattinoni, L. Lineage relationship of effector and memory T cells. Curr. Opin. Immunol. 25, 556-563 (2013
145. Polyak, K. Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat. Med. 20, 344-346 (2014
146. Furnari, F. B., Cloughesy, T. F., Cavenee, W. K., & Mischel, P. S. Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma. Nat. Rev. Cancer 15, 302-310 (2015
147. Nowell, P. The clonal evolution of tumor cell populations. Science 194, 23-28 (1976
148. Aktipis, C. A., Boddy, A. M., Gatenby, R. A., Brown, J. S., & Maley, C. C. Life history trade-offs in cancer evolution. Nat. Rev. Cancer 13, 883-892 (2013
149. Almendro, V., Marusyk, A., & Polyak, K. Cellular heterogeneity and molecular evolution in cancer. Annu. Rev. Pathol. 8, 277-302 (2013
150. Lawrence, M. S., et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495-501 (2014
151. Hanahan, D., & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646-674 (2011
152. Quail, D. F., & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423-1437 (2013
153. Semenza, G. L. Cancer-stromal cell interactions mediated by hypoxia-inducible factors promote angiogenesis, lymphangiogenesis, and metastasis. Oncogene 32, 4057-4063 (2013
154. Yaffe, M. B. The scientific drunk at the lamppost: massive sequencing efforts in cancer discovery and treatment. Sci. Signal. 6, e13 (2013
155. Brennan, C. W., et al. The somatic genetic landscape of glioblastoma. Cell 155, 462-477 (2013
156. Chin, L., et al. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068 (2008
157. Inda, M. d.-M., et al. Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev. 24, 1731-1745 (2010
158. Zadeh, G., Bhat, K. P. L., & Aldape, K. EGFR and EGFRvIII in glioblastoma: partners in crime. Cancer Cell 24, 403-404 (2013
159. Bachoo, R. M., et al. Epidermal growth factor receptor and Ink4a/Arf: convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis. Cancer Cell 1, 269-277 (2002
160. Sottoriva, A., et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009-4014 (2013
161. Gill, B. J., et al. MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma. Proc. Natl Acad. Sci. USA 111, 12550-12555 (2014
162. Klages, R., et al Nonequilibrium Statistical Physics of Small Systems: Fluctuation Relations and Beyond (Wiley, 2013
163. Losick, R., & Desplan, C. Stochasticity and cell fate. Science 320, 65-68 (2008
164. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L., & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622-1625 (2004
165. Aguirre-Ghiso, J. A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer 7, 834-846 (2007
166. Kholodenko, B., Yaffe, M. B., & Kolch, W. Computational approaches for analyzing information flow in biological networks. Sci. Signal. 5, re1 (2012