Translational research in oncology - 10 years of progress and future prospects

Nature Reviews Clinical Oncology

Volumn 11, Issue 11, 2014, Pages 649-662

  Doroshow, James H ^a   Kummar, Shivaani ^a  

^a NATIONAL CANCER INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTINEOPLASTIC AGENT; BIOLOGICAL MARKER; CYTOTOXIC AGENT; CYTOTOXIC T LYMPHOCYTE ANTIGEN 4 ANTIBODY; FLUCYTOSINE DEOXYRIBOSIDE; GRANULOCYTE MACROPHAGE COLONY STIMULATING FACTOR VACCINE; IPILIMUMAB; NIVOLUMAB; PLACEBO; PROGRAMMED DEATH 1 RECEPTOR ANTIBODY; SIPULEUCEL T; TAMOXIFEN; TRASTUZUMAB; UNCLASSIFIED DRUG;

ADVANCED CANCER; ANTINEOPLASTIC ACTIVITY; BREAST CANCER; CANCER DIAGNOSIS; CANCER GENETICS; CANCER IMMUNOTHERAPY; CANCER RESEARCH; CANCER RESISTANCE; CANCER THERAPY; CARCINOGENESIS; CASTRATION RESISTANT PROSTATE CANCER; CLINICAL FEATURE; CRANIAL NEUROPATHY; DERMATITIS; DNA SEQUENCE; DRUG RESEARCH; ENDOCRINE DISEASE; ENTEROCOLITIS; GENE EXPRESSION; GENETIC ANALYSIS; GENETIC ENGINEERING; GENETIC HETEROGENEITY; HEPATITIS; HUMAN; IMMUNOPATHOLOGY; INFLAMMATION; KIDNEY CANCER; MALIGNANT NEOPLASTIC DISEASE; MELANOMA; METASTATIC MELANOMA; MOLECULAR IMAGING; MOLECULAR MODEL; MOLECULARLY TARGETED THERAPY; MULTICENTER STUDY (TOPIC); NON SMALL CELL LUNG CANCER; NONHUMAN; ONCOLOGY; OUTCOME ASSESSMENT; OVERALL SURVIVAL; PHARMACODYNAMICS; PHASE 1 CLINICAL TRIAL (TOPIC); PHASE 2 CLINICAL TRIAL (TOPIC); PHASE 3 CLINICAL TRIAL (TOPIC); PNEUMONIA; PROSTATE CANCER; PROTEIN EXPRESSION; RANDOMIZED CONTROLLED TRIAL (TOPIC); RASH; REVIEW; SIGNAL TRANSDUCTION; SOLID TUMOR; THYROIDITIS; TRANSLATIONAL RESEARCH; TUMOR XENOGRAFT; DRUG DEVELOPMENT; FORECASTING; NEOPLASMS; PERSONALIZED MEDICINE; TRENDS;

ANTINEOPLASTIC AGENTS; DRUG DISCOVERY; FORECASTING; HUMANS; INDIVIDUALIZED MEDICINE; MEDICAL ONCOLOGY; MOLECULAR TARGETED THERAPY; NEOPLASMS; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84908497841     PISSN: 17594774     EISSN: 17594782     Source Type: Journal    
DOI: 10.1038/nrclinonc.2014.158     Document Type: Review

References (147)

1. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646-674 (2011).
2. Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823-837 (2009).
3. Lieu, C. H., Tan, A. C., Leong, S., Diamond, J. R. & Eckhardt, S. G. From bench to bedside: lessons learned in translating preclinical studies in cancer drug development. J. Natl Cancer Inst. 105, 1441-1456 (2013).
4. Dowell, J. E. & Minna, J. D. The impact of epidermal-growth-factor-receptor mutations in response to lung-cancer therapy. Nat. Clin. Pract. Oncol. 1, 2-3 (2004).
5. Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129-2139 (2004).
6. Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497-1500 (2004).
7. Pao, W. et al. EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl Acad. Sci. USA 101, 13306-13311 (2004).
8. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783-792 (2001).
9. Druker, B. J. Perspectives on the development of imatinib and the future of cancer research. Nat. Med. 15, 1149-1152 (2009).
10. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817-2826 (2004).
11. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333-339 (2013).
12. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993-998 (2010).
13. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603-607 (2012).
14. Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570-575 (2012).
15. Abaan, O. D. et al. The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res. 73, 4372-4382 (2013).
16. Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693-1703 (2010).
17. Flaherty, K. T. et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363, 809-819 (2010).
18. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883-892 (2012).
19. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917-921 (2005).
20. Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123-134 (2009).
21. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252-264 (2012).
22. Hamid, O. et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med. 369, 134-144 (2013).
23. + T cells in a patient with epithelial cancer. Science 344, 641-645 (2014).
24. Palechor-Ceron, N. et al. Radiation induces diffusible feeder cell factor(s) that cooperate with ROCK inhibitor to conditionally reprogram and immortalize epithelial cells. Am. J. Pathol. 183, 1862-1870 (2013).
25. Perez-Mancera, P. A., Guerra, C., Barbacid, M. & Tuveson, D. A. What we have learned about pancreatic cancer from mouse models. Gastroenterology 142, 1079-1092 (2012).
26. Siolas, D. & Hannon, G. J. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 73, 5315-5319 (2013).
27. Hodgkinson, C. L. et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat. Med. 20, 897-903 (2014).
28. Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17-37 (2013).
29. Abrams, J. et al. National Cancer Institute's precision medicine initiatives for the new National Clinical Trials Network. Am. Soc. Clin. Oncol. Educ. Book 34, 71-76 (2014).
30. Iyer, G. et al. Genome sequencing identifies a basis for everolimus sensitivity. Science 338, 221 (2012).
31. Sausville, E. A. & Burger, A. M. Contributions of human tumor xenografts to anticancer drug development. Cancer Res. 66, 3351-3354 (2006).
32. Johnson, J. I. et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br. J. Cancer 84, 1424-1431 (2001).
33. Frese, K. K. & Tuveson, D. A. Maximizing mouse cancer models. Nat. Rev. Cancer 7, 645-658 (2007).
34. Singh, M., Murriel, C. L. & Johnson, L. Genetically engineered mouse models: closing the gap between preclinical data and trial outcomes. Cancer Res. 72, 2695-2700 (2012).
35. Navas, C. et al. EGF receptor signaling is essential for K-Ras oncogene-driven pancreatic ductal adenocarcinoma. Cancer Cell 22, 318-330 (2012).
36. Tentler, J. J. et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338-350 (2012).
37. Li, S. et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Cell Rep. 4, 1116-1130 (2013).
38. Kopetz, S., Lemos, R. & Powis, G. The promise of patient-derived xenografts: the best laid plans of mice and men. Clin. Cancer Res. 18, 5160-5162 (2012).
39. Suprynowicz, F. A. et al. Conditionally reprogrammed cells represent a stem-like state of adult epithelial cells. Proc. Natl Acad. Sci. USA 109, 20035-20040 (2012).
40. Liu, X. et al. ROCK inhibitor and feeder cells induce the conditional reprogramming of epithelial cells. Am. J. Pathol. 180, 599-607 (2012).
41. Sato, T. & Clevers, H. Growing self-organizing mini-guts from a single intestinal stem cell: mechanism and applications. Science 340, 1190-1194 (2013).
42. Sachs, N. & Clevers, H. Organoid cultures for the analysis of cancer phenotypes. Curr. Opin. Genet. Dev. 24, 68-73 (2014).
43. Trinchieri, G. Cancer and inflammation: an old intuition with rapidly evolving new concepts. Annu. Rev. Immunol. 30, 677-706 (2012).
44. Guerra, C. et al. Pancreatitis-induced inflammation contributes to pancreatic cancer by inhibiting oncogene-induced senescence. Cancer Cell 19, 728-739 (2011).
45. Grivennikov, S. I. Inflammation and colorectal cancer: colitis-associated neoplasia. Semin. Immunopathol. 35, 229-244 (2013).
46. Wu, Y., Antony, S., Meitzler, J. L. & Doroshow, J. H. Molecular mechanisms underlying chronic inflammation-associated cancers. Cancer Lett. 345, 164-173 (2014).
47. Thorat, M. A. & Cuzick, J. Role of aspirin in cancer prevention. Curr. Oncol. Rep. 15, 533-540 (2013).
48. Iida, N. et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342, 967-970 (2013).
49. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029-1033 (2009).
50. Jones, R. G. & Thompson, C. B. Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes Dev. 23, 537-548 (2009).
51. Galluzzi, L., Kepp, O., Vander Heiden, M. G. & Kroemer, G. Metabolic targets for cancer therapy. Nat. Rev. Drug Discov. 12, 829-846 (2013).
52. Vander Heiden, M. G. Exploiting tumor metabolism: challenges for clinical translation. J. Clin. Invest. 123, 3648-3651 (2013).
53. Kaelin, W. G. Jr & McKnight, S. L. Influence of metabolism on epigenetics and disease. Cell 153, 56-69 (2013).
54. Rohle, D. et al. An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340, 626-630 (2013).
55. Wang, F. et al. Targeted inhibition of mutant IDH2 in leukemia cells induces cellular differentiation. Science 340, 622-626 (2013).
56. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 431, 931-945 (2004).
57. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495-501 (2014).
58. Chung, C. C. & Chanock, S. J. Current status of genome-wide association studies in cancer. Hum. Genet. 130, 59-78 (2011).
59. Kim, H. S., Minna, J. D. & White, M. A. GWAS meets TCGA to illuminate mechanisms of cancer predisposition. Cell 152, 387-389 (2013).
60. Robb, J. A. et al. A call to standardize preanalytic data elements for biospecimens. Arch. Pathol. Lab. Med. 138, 526-537 (2014).
61. Engel, K. B., Vaught, J. & Moore, H. M. National Cancer Institute biospecimen evidence-based practices: a novel approach to pre-analytical standardization. Biopreserv. Biobank. 12, 148-150 (2014).
62. Baker, A. F. et al. Stability of phosphoprotein as a biological marker of tumor signaling. Clin. Cancer Res. 11, 4338-4340 (2005).
63. Kinders, R. J. et al. Preclinical modeling of a phase 0 clinical trial: qualification of a pharmacodynamic assay of poly (ADP-ribose) polymerase in tumor biopsies of mouse xenografts. Clin. Cancer Res. 14, 6877-6885 (2008).
64. Park, S. R. et al. Validation of a hypoxia-inducible factor-1 alpha specimen collection procedure and quantitative enzyme-linked immunosorbent assay in solid tumor tissues. Anal. Biochem. 459, 1-11 (2014).
65. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546-1558 (2013).
66. Shen, H. & Laird, P. W. Interplay between the cancer genome and epigenome. Cell 153, 38-55 (2013).
67. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature http:// dx.doi.org/10.1038/nature13438.
68. Callaway, E. Global genomic data-sharing effort kicks off. Nature http://dx.doi.org/10.1038/ nature.2014.14826.
69. Meacham, C. E. & Morrison, S. J. Tumour heterogeneity and cancer cell plasticity. Nature 501, 328-337 (2013).
70. Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225-233 (2014).
71. Bedard, P. L., Hansen, A. R., Ratain, M. J. & Siu, L. L. Tumour heterogeneity in the clinic. Nature 501, 355-364 (2013).
72. Yap, T. A., Gerlinger, M., Futreal, P. A., Pusztai, L. & Swanton, C. Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps10 (2012).
73. Krebs, M. G. et al. Molecular analysis of circulating tumour cells-biology and biomarkers. Nat. Rev. Clin. Oncol. 11, 129-144 (2014).
74. Haber, D. A. & Velculescu, V. E. Blood-based analyses of cancer: circulating tumor cells and circulating tumor DNA. Cancer Discov. 4, 650-661 (2014).
75. Wang, L. H. et al. Monitoring drug-induced γH2AX as a pharmacodynamic biomarker in individual circulating tumor cells. Clin. Cancer Res. 16, 1073-1084 (2010).
76. Kummar, S., Gutierrez, M., Doroshow, J. H. & Murgo, A. J. Drug development in oncology: classical cytotoxics and molecularly targeted agents. Br. J. Clin. Pharmacol. 62, 15-26 (2006).
77. Jeong, W., Doroshow, J. H. & Kummar, S. United States Food and Drug Administration approved oral kinase inhibitors for the treatment of malignancies. Curr. Probl. Cancer 37, 110-144 (2013).
78. Huang, M., Shen, A., Ding, J. & Geng, M. Molecularly targeted cancer therapy: some lessons from the past decade. Trends Pharmacol. Sci. 35, 41-50 (2014).
79. Kummar, S. et al. Compressing drug development timelines in oncology using phase '0' trials. Nat. Rev. Cancer 7, 131-139 (2007).
80. Yap, T. A., Sandhu, S. K., Workman, P. & de Bono, J. S. Envisioning the future of early anticancer drug development. Nat. Rev. Cancer 10, 514-523 (2010).
81. [No authors listed] Pharmacogenomics at work. Nat. Biotechnol. 16, 885 (1998).
82. Dedes, K. J. et al. Synthetic lethality of PARP inhibition in cancers lacking BRCA1 and BRCA2 mutations. Cell Cycle 10, 1192-1199 (2011).
83. Kim, E. S. et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 1, 44-53 (2011).
84. Sequist, L. V. et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci. Transl. Med. 3, 75ra26 (2011).
85. Dias-Santagata, D. et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol. Med. 2, 146-158 (2010).
86. Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011).
87. US National Library of Medicine. Clinicaltrials.gov [online], http://clinicaltrials.gov/show/ NCT02154490 ( 2014).
88. Conley, B. A. & Doroshow, J. H. Molecular analysis for therapy choice: NCI MATCH. Semin. Oncol. 41, 297-299 (2014).
89. Cohen, R. L. & Settleman, J. From cancer genomics to precision oncology-tissue's still an issue. Cell 157, 1509-1514 (2014).
90. Sarker, D. & Workman, P. Pharmacodynamic biomarkers for molecular cancer therapeutics. Adv. Cancer Res. 96, 213-268 (2007).
91. Doroshow, J. H. & Parchment, R. E. Oncologic phase 0 trials incorporating clinical pharmacodynamics: from concept to patient. Clin. Cancer Res. 14, 3658-3663 (2008).
92. Gainor, J. F., Longo, D. L. & Chabner, B. A. Pharmacodynamic biomarkers: falling short of the mark? Clin. Cancer Res. 20, 2587-2594 (2014).
93. Kinders, R. et al. Implementation of validated pharmacodynamic assays in multiple laboratories: challenges, successes, and limitations. Clin. Cancer Res. 20, 2578-2586 (2014).
94. Mateo, J., Ong, M., Tan, D. S., Gonzalez, M. A. & de Bono, J. S. Appraising iniparib, the PARP inhibitor that never was-what must we learn? Nat. Rev. Clin. Oncol. 10, 688-696 (2013).
95. Kinders, R. J. et al. Preclinical modeling of a phase 0 clinical trial: qualification of a pharmacodynamic assay of poly (ADP-ribose) polymerase in tumor biopsies of mouse xenografts. Clin. Cancer Res. 14, 6877-6885 (2008).
96. Kinders, R. et al. Development of a validated immunofluorescence assay for γH2AX as a pharmacodynamic marker of topoisomerase I inhibitor activity. Clin. Cancer Res. 16, 5447-5457 (2010).
97. Kummar, S. et al. Phase I study of ABT-888, a PARP inhibitor, in combination with topotecan hydrochloride in adults with refractory solid tumors and lymphomas. Cancer Res. 71, 5626-5634 (2011).
98. Marrero, A. M. et al. A multiplex quantitative immunofluorescence assay for DNA damage repair in response to cytotoxic treatment [abstract]. Cancer Res. 72 (Suppl. 1), a3620 (2012).
99. Akbani, R. et al. Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (reverse phase protein array) society. Mol. Cell Proteomics 13, 1625-1643 (2014).
100. Hayashi, N. et al. Reverse-phase protein array for prediction of patients at low risk of developing bone metastasis from breast cancer. Oncologist http://dx.doi.org/10.1634/ theoncologist.2014-0099.
101. Kelloff, G. J. & Sigman, C. C. Cancer biomarkers: selecting the right drug for the right patient. Nat. Rev. Drug Discov. 11, 201-214 (2012).
102. Molinari, F. et al. Increased detection sensitivity for KRAS mutations enhances the prediction of anti-EGFR monoclonal antibody resistance in metastatic colorectal cancer. Clin. Cancer Res. 17, 4901-4914 (2011).
103. Drukker, C. A. et al. Long-term impact of the 70-gene signature on breast cancer outcome. Breast Cancer Res. Treat. 143, 587-592 (2014).
104. Micheel, C. M., Nass, S. J. & Omenn, G. S. Evolution of tranlational omics: lessons learned and the path forward (The National Academies Press, 2012).
105. McShane, L. M. et al. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BMC Med. 11, 220 (2013).
106. Parkinson, D. R. et al. Evidence of clinical utility: an unmet need in molecular diagnostics for patients with cancer. Clin. Cancer Res. 20, 1428-1444 (2014).
107. Alifrangis, C. C. & McDermott, U. Reading between the lines: understanding drug response in the post genomic era. Mol. Oncol. http:// dx.doi.org/10.1016/j.molonc.2014.05.014.
108. Rebucci, M. & Michiels, C. Molecular aspects of cancer cell resistance to chemotherapy. Biochem. Pharmacol. 85, 1219-1226 (2013).
109. Park, S. R., Davis-Millin, M., Doroshow, J. H. & Kummar, S. Safety and feasibility of targeted agent combinations in solid tumors. Nat. Rev. Clin. Oncol. 10, 154-168 (2013).
110. Kummar, S. et al. Utilizing targeted cancer therapeutic agents in combination: novel approaches and urgent requirements. Nat. Rev. Drug Discov. 9, 843-856 (2010).
111. Holbeck, S., Collins, J. M. & Doroshow, J. H. NCI-60 combination screening matrix of approved anticancer drugs [abstract 27]. Eur. J. Cancer 48 (Suppl. 6), 11 (2012).
112. Hinrichs, C. S. & Rosenberg, S. A. Exploiting the curative potential of adoptive T-cell therapy for cancer. Immunol. Rev. 257, 56-71 (2014).
113. Maus, M. V., Grupp, S. A., Porter, D. L. & June, C. H. Antibody-modified T cells: CARs take the front seat for hematologic malignancies. Blood 123, 2625-2635 (2014).
114. Dotti, G., Gottschalk, S., Savoldo, B. & Brenner, M. K. Design and development of therapies using chimeric antigen receptor-expressing T cells. Immunol. Rev. 257, 107-126 (2014).
115. Kantoff, P. W. et al. Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N. Engl. J. Med. 363, 411-422 (2010).
116. Mishra, A., Sullivan, L. & Caligiuri, M. A. Molecular pathways: interleukin-15 signaling in health and in cancer. Clin. Cancer Res. 20, 2044-2050 (2014).
117. Robert, C. et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N. Engl. J. Med. 364, 2517-2526 (2011).
118. Fecher, L. A., Agarwala, S. S., Hodi, F. S. & Weber, J. S. Ipilimumab and its toxicities: a multidisciplinary approach. Oncologist 18, 733-743 (2013).
119. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443-2454 (2012).
120. Brahmer, J. R. et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455-2465 (2012).
121. Schiller, J. T. & Lowy, D. R. Understanding and learning from the success of prophylactic human papillomavirus vaccines. Nat. Rev. Microbiol. 10, 681-692 (2012).
122. Schiller, J. T. & Lowy, D. R. Papillomavirus-like particle vaccines. J. Natl Cancer Inst. Monogr. 50-54 (2001).
123. Lowy, D. R. & Munger, K. Prognostic implications of HPV in oropharyngeal cancer. N. Engl. J. Med. 363, 82-84 (2010).
124. Uhlman, M. A., Bing, M. T. & Lubaroff, D. M. Prostate cancer vaccines in combination with additional treatment modalities. Immunol. Res. 59, 236-242 (2014).
125. Woo, S. R. et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72, 917-927 (2012).
126. Naidoo, J., Page, D. B. & Wolchok, J. D. Immune checkpoint blockade. Hematol. Oncol. Clin. North Am. 28, 585-600 (2014).
127. Cheadle, E. J. et al. CAR T cells: driving the road from the laboratory to the clinic. Immunol. Rev. 257, 91-106 (2014).
128. Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122-133 (2013).
129. Ascierto, P. A. et al. Sequencing of BRAF inhibitors and ipilimumab in patients with metastatic melanoma: a possible algorithm for clinical use. J. Transl. Med. 10, 107 (2012).
130. Histed, S. N. et al. Review of functional/ anatomical imaging in oncology. Nucl. Med. Commun. 33, 349-361 (2012).
131. 18FDG-positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec). Eur. J. Cancer 39, 2012-2020 (2003).
132. Rosen, M. A. & Schnall, M. D. Dynamic contrast-enhanced magnetic resonance imaging for assessing tumor vascularity and vascular effects of targeted therapies in renal cell carcinoma. Clin. Cancer Res. 13, 770s-776s (2007).
133. 18F-FDG. PET Clin. 9, 345-349 (2014).
134. 89Zr-bevacizumab PET to evaluate the effect of the HSP90 inhibitor NVP-AUY922 in metastatic breast cancer patients. Clin. Cancer Res. 20, 3945-3954 (2014).
135. Esteban, J. M., Ahn, C., Battifora, H. & Felder, B. Predictive value of estrogen receptors evaluated by quantitative immunohistochemical analysis in breast cancer. Am. J. Clin. Pathol. 102, S9-S12 (1994).
136. Jordan, V. C. Proven value of translational research with appropriate animal models to advance breast cancer treatment and save lives: the tamoxifen tale. Br. J. Clin. Pharmacol. http:// dx.doi.org/10.1111/bcp.12440.
137. Linden, H. M. et al. Quantitative fluoroestradiol positron emission tomography imaging predicts response to endocrine treatment in breast cancer. J. Clin. Oncol. 24, 2793-2799 (2006).
138. US National Library of Medicine. Clinicaltrials.gov [online], http://clinicaltrials.gov/ct2/ results?term=NCT01479348 ( 2014).
139. Beumer, J. H. et al. Concentrations of the DNA methyltransferase inhibitor 5-fluoro-2'-deoxycytidine (FdCyd) and its cytotoxic metabolites in plasma of patients treated with FdCyd and tetrahydrouridine (THU). Cancer Chemother. Pharmacol. 62, 363-368 (2008).
140. Scott, C. L., Mackay, H. J. & Haluska, P. Jr. Patient-derived xenograft models in gynecologic malignancies. Am. Soc. Clin. Oncol. Educ. Book 34, 258-266 (2014).
141. Lamontanara, A. J., Gencer, E. B., Kuzyk, O. & Hantschel, O. Mechanisms of resistance to BCR-ABL and other kinase inhibitors. Biochim. Biophys. Acta 1834, 1449-1459 (2013).
142. Doroshow, J. H. Overcoming resistance to targeted anticancer drugs. N. Engl. J. Med. 369, 1852-1853 (2013).
143. Kummar, S. et al. Phase I trial of Z-endoxifen with estrogen receptor imaging in adults with refractory hormone receptor-positive breast cancer, desmoid tumors, gynecologic tumors, or other hormone receptor-positive solid tumors [abstract 591]. Eur. J. Cancer 48 (Suppl. 6), 181 (2012).
144. Bhattacharyya, S. et al. Zirconium-89 labeled panitumumab: a potential immuno-PET probe for HER1-expressing carcinomas. Nucl. Med. Biol. 40, 451-457 (2013).
145. Chu, W. et al. Development of a PET radiotracer for non-invasive imaging of the reactive oxygen species, superoxide, in vivo. Org. Biomol. Chem. 12, 4421-4431 (2014).
146. Salomonnson, E., Stacer, A. C., Ehrlich, A., Luker, K. E. & Luker, G. D. Imaging CXCL12-CXCR4 signaling in ovarian cancer therapy. PLoS ONE 8, e51500 (2013).
147. Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).