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[100] Castillero F, Castillo-Ferna´ndez O, Jimenez-Jimenez G, Fallas-Ramı´rez J, Peralta-A´lvarez MP,
Arrieta O. Cancer immunotherapy-associated hypophysitis. Future Oncol 2019;15(27):3159–69.
[101] Wang GX, Guo LQ, Gainor JF, Fintelmann FJ. Immune checkpoint inhibitors in lung cancer: imag-
ing considerations. Am J Roentgenol 2017;209(3):567–75.
[102] Cheshire SC, Board RE, Lewis AR, Gudur LD, Dobson MJ. Pembrolizumab-induced sarcoid-like
reactions during treatment of metastatic melanoma. Radiology 2018;289(2):564–7.
[103] Som A, Mandaliya R, Alsaadi D, Farshidpour M, Charabaty A, Malhotra N, et al. Immune checkpoint
inhibitor-induced colitis: a comprehensive review. World J Clin Cases 2019;7(4):405 –18.
[104] Abu-Sbeih H, Tang T, Lu Y, Thirumurthi S, Altan M, Jazaeri AA, et al. Clinical characteristics and out-
comes of immune checkpoint inhibitor-induced pancreatic injury. J Immunother Cancer 2019;7(1):31.
[105] Reynolds K, Thomas M, Dougan M. Diagnosis and management of hepatitis in patients on check-
point blockade. Oncologist 2018;23(9):991–7.
[106] Braaten TJ, Brahmer JR, Forde PM, Le D, Lipson EJ, Naidoo J, et al. Immune checkpoint inhibitor-
induced inflammatory arthritis persists after immunotherapy cessation. Ann Rheum Dis 2020;79
(3):332–8.
[107] Palaskas N, Lopez-Mattei J, Durand JB, Iliescu C, Deswal A. Immune checkpoint inhibitor myocar-
ditis: pathophysiological characteristics, diagnosis, and treatment. J Am Heart Assoc 2020;9(2),
e013757.
[108] Krasniqi E, Barchiesi G, Pizzuti L, Mazzotta M, Venuti A, Maugeri-Sacca`M, et al. Immunotherapy in
HER2-positive breast cancer: state of the art and future perspectives. J Hematol Oncol 2019;12
(1):111.
[109] Cilliers C, Menezes B, Nessler I, Linderman J, Thurber GM. Improved tumor penetration and single-
cell targeting of antibody-drug conjugates increases anticancer efficacy and host survival. Cancer Res
2018;78(3):758–68.
[110] Bhusari P, Vatsa R, Singh G, Parmar M, Bal A, Dhawan DK, et al. Development of Lu-177-
trastuzumab for radioimmunotherapy of HER2 expressing breast cancer and its feasibility assessment
in breast cancer patients. Int J Cancer 2017;140(4):938–47.
[111] Mortimer JE, Bading JR, Park JM, Frankel PH, Carroll MI, Tran TT, et al. Tumor uptake of 64Cu-
DOTA-trastuzumab in patients with metastatic breast cancer. J Nucl Med 2017;59(1):38–43.
[112] Zettlitz KA, Tavare R, Tsai W-TK, Yamada RE, Ha NS, Collins J, et al. 18F-labeled anti-human
CD20 cys-diabody for same-day immunoPET in a model of aggressive B cell lymphoma in human
CD20 transgenic mice. Eur J Nucl Med Mol Imaging 2019;46(2):489–500.
[113] Marciscano AE, Thorek DLJ. Role of noninvasive molecular imaging in determining response. Adv
Rad Oncol 2018;3(4):534–47.
[114] Bailly C, Chalopin B, Gouard S, Carlier T, Sae¨c PR, Marionneau-Lambot S, et al. ImmunoPET in
multiple myeloma—What? So What? Now What? Cancers (Basel) 2020;12(6).
[115] Rios X, Compte M, Go´mez-Vallejo V, Cossı´o U, Baz Z, Morcillo M, et al. Immuno-PET imaging
and pharmacokinetics of an Anti-CEA scFv-based trimerbody and its monomeric counterpart in
human gastric carcinoma-bearing mice. Mol Pharm 2019;16(3):1025–35.
[116] Burley TA, Da Pieve C, Martins CD, Ciobota DM, Allott L, Oyen WJG, et al. Affibody-based PET
imaging to guide EGFR-targeted cancer therapy in head and neck squamous cell cancer models.
J Nucl Med 2019;60(3):353–
61.
[117] Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D. Dendritic cells in cancer
immunology and immunotherapy. Nat Rev Immunol 2020;20(1):7–24.
[118] Wang B, Sun C, Wang S, Shang N, Figini M, Ma Q, et al. Image-guided dendritic cell-based vaccine
immunotherapy in murine carcinoma models. Am J Transl Res 2017;9(10):4564–73.
[119] Lee SB, Lee HW, Lee H, Jeon YH, Lee SW, Ahn BC, et al. Tracking dendritic cell migration into
lymph nodes by using a novel PET probe (18)F-tetrafluoroborate for sodium/iodide symporter.
EJNMMI Res 2017;7(1):32.
[120] Lee SB, Ahn SB, Lee S-W, Jeong SY, Ghilsuk Y, Ahn B-C, et al. Radionuclide-embedded gold
nanoparticles for enhanced dendritic cell-based cancer immunotherapy, sensitive and quantitative
tracking of dendritic cells with PET and Cerenkov luminescence. NPG Asia Mater 2016;8(6).
e281-e.
461Image-guided cancer immunotherapy
462 Thomas S.C. Ng and Miles A. Miller
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
[121] Lee SB, Lee S-W, Jeong SY, Yoon G, Cho SJ, Kim SK, et al. Engineering of radioiodine-labeled gold
core–shell nanoparticles as efficient nuclear medicine imaging agents for trafficking of dendritic cells.
ACS Appl Mater Interfaces 2017;9(10):8480–9.
[122] Mou Y, Chen B, Zhang Y, Hou Y, Xie H, Xia G, et al. Influence of synthetic superparamagnetic iron
oxide on dendritic cells. Int J Nanomed 2011;6:1779–86.
[123] Tavare R, Sagoo P, Varama G, Tanriver Y, Warely A, Diebold SS, et al. Monitoring of in vivo func-
tion of superparamagnetic iron oxide labelled murine dendritic cells during anti-tumour vaccination.
PLoS One 2011;6(5), e19662.
[124] Kim HS, Woo J, Lee JH, Joo HJ, Choi Y, Kim H, et al. In vivo tracking of dendritic cell using MRI
reporter gene, Ferritin. PLoS One 2015;10(5), e0125291.
[125] Waiczies H, Guenther M, Skodowski J, Lepore S, Pohlmann A, Niendorf T, et al. Monitoring den-
dritic cell migration using 19F/1H magnetic resonance imaging. J Vis Exp 2013;73, e50251.
[126] Zhou J, Tang Z, Gao S, Li C, Feng Y, Zhou X. Tumor-associated macrophages: recent insights and
therapies. Front Oncol 2020;10:188.
[127] WangSJ,LiR,NgTSC,LuthriaG,OudinM,PrytyskachM,etal.Efficientblockadeoflocally
reciprocated tumor-macrophage signaling using a TAM-avid monotherapy. Sci Adv 2020;6(21):
eaaz8521.
[128] Pfirschke C, Engblom C, Rickelt S, Cortez-Retamozo V, Garris C, Pucci F, et al. Immunogenic
chemotherapy sensitizes tumors to checkpoint blockade therapy. Immunity 2016;44(2):343–54.
[129] Miller MA, Gadde S, Pfirschke C, Engblom C, Sprachman MM, Kohler RH, et al. Predicting ther-
apeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle. Sci
Transl Med 2015;7(314):314ra183.
[130] Miller MA, Chandra R, Cuccarese MF, Pfirschke C, Engblom C, Stapleton S, et al. Radiation therapy
primes tumors for nanotherapeutic delivery via macrophage-mediated vascular bursts. Sci Transl Med
2017;9(392).
[131] Cuccarese MF, Dubach JM, Pfirschke C, Engblom C, Garris C, Miller MA, et al. Heterogeneity of
macrophage infiltration and therapeutic response in lung carcinoma revealed by 3D organ imaging.
Nat Commun 2017;8:14293.
[132] Rodell CB, Arlauckas SP, Cuccarese MF, Garris CS, Li R, Ahmed MS, et al. TLR7/8-agonist-loaded
nanoparticles promote the polarization of tumour-associated macrophages to enhance cancer immunotherapy. Nat Biomed Eng 2018;2:578–88.
[133] Hudgins PA, Anzai Y, Morris MR, Lucas MA. Ferumoxtran-10, a superparamagnetic iron oxide as a
magnetic resonance enhancement agent for imaging lymph nodes: a phase 2 dose study. Am
J Neuroradiol 2002;23(4):649–56.
[134] Danhier P, Deumer G, Joudiou N, Bouzin C, Lev^eque P, Haufroid V, et al. Contribution of mac-
rophages in the contrast loss in iron oxide-based MRI cancer cell tracking studies. Oncotarget 2017;8
(24):38876–85.
[135] Wang G, Serkova NJ, Groman EV, Scheinman RI, Simberg D. Feraheme (Ferumoxytol) Is recog-
nized by proinflammatory and anti-inflammatory macrophages via scavenger receptor type AI/II. Mol
Pharm 2019;16(10):4274–81.
[136] Iv M, Samghabadi P, Holdsworth S, Gentles A, Rezaii P, Harsh G, et al. Quantification of macro-
phages in high-grade gliomas by using ferumoxytol-enhanced MRI: a pilot study. Radiology
2019;290(1):198–206.
[137] Mohanty S, Yerneni K, Theruvath JL, Graef CM, Nejadnik H, Lenkov O, et al. Nanoparticle
enhanced MRI can monitor macrophage response to CD47 mAb immunotherapy in osteosarcoma.
Cell Death Dis 2019;10(2):36.
[138] Aghighi M, Theruvath AJ, Pareek A, Pisani LL, Alford R, Muehe AM, et al. Magnetic resonance
imaging of tumor-associated macrophages: clinical translation. Clin Cancer Res 2018;24(17):4110–8.
[139] Makela AV, Gaudet JM, Foster PJ. Quantifying tumor associated macrophages in breast cancer: a
comparison of iron and fluorine-based MRI cell tracking. Sci Rep 2017;7(1):42109.
[140] Miller MA, Zheng YR, Gadde S, Pfirschke C, Zope H, Engblom C, et al. Tumour-associated mac-
rophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug. Nat Commun
2015;6:8692.
[141] Daldrup-Link HE, Golovko D, Ruffell B, Denardo DG, Castaneda R, Ansari C, et al. MRI of tumor-
associated macrophages with clinically applicable iron oxide nanoparticles. Clin Cancer Res 2011;17
(17):5695–704.
[142] Zanganeh S, Hutter G, Spitler R, Lenkov O, Mahmoudi M, Shaw A, et al. Iron oxide nanoparticles
inhibit tumour growth by inducing pro-inflammatory macrophage polarization in tumour tissues. Nat
Nanotechnol 2016;11(11):986–94.
[143] Kim HY, Li R, Ng TSC, Courties G, Rodell CB, Prytyskach M, et al. Quantitative imaging of
tumor-associated macrophages and their response to therapy using (64)Cu-labeled macrin. ACS Nano
2018;12(12):12015–29.
[144] Perez-Medina C, Tang J, Abdel-Atti D, Hogstad B, Merad M, Fisher EA, et al. PET imaging of
tumor-associated macrophages with 89Zr-labeled high-density lipoprotein nanoparticles. J Nucl
Med 2015;56(8):1272–7.
[145] Movahedi K, Schoonooghe S, Laoui D, Houbracken I, Waelput W, Breckpot K, et al. Nanobody-
based targeting of the macrophage mannose receptor for effective in vivo imaging of tumor-associated
macrophages. Cancer Res 2012;72(16):4165–77.
[146] Blykers A, Schoonooghe S, Xavier C, D’hoe K, Laoui D, D’Huyvetter M, et al. PET imaging of
macrophage mannose receptor–expressing macrophages in tumor stroma using 18F-radiolabeled
camelid single-domain antibody fragments. J Nucl Med 2015;56(8):1265–71.
[147] Li Y, Wu H, Ji B, Qian W, Xia S, Wang L, et al. Targeted imaging of CD206 expressing tumor-
associated M2-like macrophages using mannose-conjugated antibiofouling magnetic iron oxide
nanoparticles. ACS Appl Biomater 2020;3(7):4335 –47.
[148] Eichendorff S, Svendsen P, Bender D, Keiding S, Christensen EI, Deleuran B, et al. Biodistribution
and PET imaging of a novel [68Ga]-anti-CD163-antibody conjugate in rats with collagen-induced
arthritis and in controls. Mol Imaging Biol 2015;17(1):87–93.
[149] Verweij NJF, Yaqub M, Bruijnen STG, Pieplenbosch S, Ter Wee MM, Jansen G, et al. First in man
study of [(18)F]fluoro-PEG-folate PET: a novel macrophage imaging technique to visualize rheuma-
toid arthritis. Sci Rep 2020;10(1):1047.
[150] Terry SY, Boerman OC, Gerrits D, Franssen GM, Metselaar JM, Lehmann S, et al.
111
In-antiF4/80-A3-1 antibody: a novel tracer to image macrophages. Eur J Nucl Med Mol Imaging
2015;42(9):1430–8.
[151] Lee HW, Jeon YH, Hwang MH, Kim JE, Park TI, Ha JH, et al. Dual reporter gene imaging for track-
ing macrophage migration using the human sodium iodide symporter and an enhanced firefly luciferase in a murine inflammation model. Mol Imaging Biol 2013;15(6):703–12.
[152] Shimasaki N, Jain A, Campana D. NK cells for cancer immunotherapy. Nat Rev Drug Discov
2020;19(3):200–18.
[153] Hu W, Wang G, Huang D, Sui M, Xu Y. Cancer immunotherapy based on natural killer cells: current
progress and new opportunities. Front Immunol 2019;10(1205).
[154] Sta Maria NS, Barnes SR, Jacobs RE. In vivo monitoring of natural killer cell trafficking during tumor
immunotherapy. Mag Reson Insights 2014;7:15–21.
[155] Ntziachristos V, Bremer C, Weissleder R. Fluorescence imaging with near-infrared light: new tech-
nological advances that enable in vivo molecular imaging. Eur Radiol 2003;13(1):195–208.
[156] Mallett CL, McFadden C, Chen Y, Foster PJ. Migration of iron-labeled KHYG-1 natural killer cells
to subcutaneous tumors in nude mice, as detected by magnetic resonance imaging. Cytotherapy
2012;14(6):743–51.
[157] Sheu AY, Zhang Z, Omary RA, Larson AC. MRI-monitored transcatheter intra-arterial delivery of
SPIO-labeled natural killer cells to hepatocellular carcinoma: preclinical studies in a rodent model.
Invest Radiol 2013;48(6):492–9.
[158] Daldrup-Link HE, Meier R, Rudelius M, Piontek G, Piert M, Metz S, et al. In vivo tracking of genet-
ically engineered, anti-HER2/neu directed natural killer cells to HER2/neu positive mammary
tumors with magnetic resonance imaging. Eur Radiol 2005;15(1):4–13.
[159] Li K, Gordon AC, Zheng L, Li W, Guo Y, Sun J, et al. Clinically applicable magnetic-labeling of
natural killer cells for MRI of transcatheter delivery to liver tumors: preclinical validation for clinical
translation. Nanomedicine (London, England) 2015;10(11):1761–74.
[160] Somanchi SS, Kennis BA, Gopalakrishnan V, Lee DA, Bankson JA. In vivo (19)F-magnetic resonance
imaging of adoptively transferred NK cells. Methods Mol Biol 2016;1441:317–32.
463Image-guided cancer immunotherapy
464 Thomas S.C. Ng and Miles A. Miller
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
[161] Bouchlaka MN, Ludwig KD, Gordon JW, Kutz MP, Bednarz BP, Fain SB, et al. (19)F-MRI for
monitoring human NK cells in vivo. Oncoimmunology 2016;5(5), e1143996.
[162] Melder RJ, Brownell AL, Shoup TM, Brownell GL, Jain RK. Imaging of activated natural killer cells in
mice by positronemissiontomography: preferential uptake in tumors.Cancer Res 1993;53(24):5867–71.
[163] Brand JM, Meller B, Von Hof K, Luhm J, B€ahre M, Kirchner H, et al. Kinetics and organ distribution
of allogeneic natural killer lymphocytes transfused into patients suffering from renal cell carcinoma.
Stem Cells Dev 2004;13(3):307–14.
[164] Varani M, Auletta S, Signore A, Galli F. State of the art of natural killer cell imaging: a systematic
review. Cancers (Basel) 2019;11(7).
[165] Hercend T, Farace F, Baume D, Charpentier F, Droz JP, Triebel F, et al. Immunotherapy with
lymphokine-activated natural killer cells and recombinant interleukin-2: a feasibility trial in metastatic
renal cell carcinoma. J Biol Response Mod 1990;9(6):546–55.
[166] Matera L, Galetto A, Bello M, Baiocco C, Chiappino I, Castellano G, et al. In vivo migration of
labeled autologous natural killer cells to liver metastases in patients with colon carcinoma. J Transl
Med 2006;4:49.
[167] Galli F, Rapisarda AS, Stabile H, Malviya G, Manni I, Bonanno E, et al. In vivo imaging of natural
killer cell trafficking in tumors. J Nucl Med 2015;56(10):1575–80.
[168] Shaffer T, Gambhir SS, Aalipour A, Schurch C. PET imaging of the natural killer cell activation recep-
tor NKp30. J Nucl Med 2020;61(9):1348–54.
[169] Romero D. B cells and TLSs facilitate a response to ICI. Nat Rev Clin Oncol 2020;17(4):195.
[170] Weiner GJ. Rituximab: mechanism of action. Semin Hematol 2010;47(2):115–23.
[171] Milenic DE, Brady ED, Brechbiel MW. Antibody-targeted radiation cancer therapy. Nat Rev Drug
Discov 2004;3(6):488–99.
[172] Thorek DLJ, Tsao PY, Arora V, Zhou L, Eisenberg RA, Tsourkas A. In vivo, multimodal imaging of
B cell distribution and response to antibody immunotherapy in mice. PLoS One 2010;5(5), e10655.
[173] Dias CR, Jeger S, Osso Jr JA, M €uller C, De Pasquale C, Hohn A, et al. Radiolabeling of rituximab
with (188)Re and (99m)Tc using the tricarbonyl technology. Nucl Med Biol 2011;38(1):19–28.
[174] Graf N, Li Z, Herrmann K, Aichler M, Slawska J, Walch A, et al. Preclinical evaluation of CD40-
directed immunotherapy in B-cell lymphoma using ( 18 F)fluorothymidine-PET. Appl Med Infor-
matics 2015;05:17–28.
[175] Voltin CA, Mettler J, Grosse J, Dietlein M, Baues C, Schmitz C, et al. FDG-PET imaging for hodgkin
and diffuse large B-cell lymphoma—an updated overview. Cancers (Basel) 2020;12(3).
[176] Beatty GL, Torigian DA, Chiorean EG, Saboury B, Brothers A, Alavi A, et al. A phase I study of an
agonist CD40 monoclonal antibody (CP-870,893) in combination with gemcitabine in patients with
advanced pancreatic ductal adenocarcinoma. Clin Cancer Res 2013;19(22):6286–95.
[177] Waldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to
clinical practice. Nat Rev Immunol 2020;20(11):651–68.
[178] Arina A, Beckett M, Fernandez C, Zheng W, Pitroda S, Chmura SJ, et al. Tumor-reprogrammed
resident T cells resist radiation to control tumors. Nat Commun 2019;10(1):3959.
[179] Larimer BM, Wehrenberg-Klee E, Caraballo A, Mahmood U. Quantitative CD3 PET imaging pre-
dicts tumor growth response to anti-CTLA-4 therapy. J Nucl Med 2016;57(10):1607–11.
[180] Pandit-Taskar N, Postow MA, Hellmann MD, Harding JJ, Barker CA, O’Donoghue JA, et al. First-
in-humans imaging with (89)Zr-Df-IAB22M2C anti-CD8 minibody in patients with solid malignan-
cies: preliminary pharmacokinetics, biodistribution, and lesion targeting. J Nucl Med 2020;61
(4):512–9.
[181] Kass I, Buckle AM, Borg NA. Understanding the structural dynamics of TCR-pMHC interactions.
Trends Immunol 2014;35(12):604–12.
[182] Woodham AW, Zeigler SH, Zeyang EL, Kolifrath SC, Cheloha RW, Rashidian M, et al. In vivo
detection of antigen-specific CD8(+) T cells by immuno-positron emission tomography. Nat
Methods 2020;17(10):1025–32.
[183] Hartimath SV, Manuelli V, Zijlma R, Signore A, Nayak TK, Freimoser-Grundschober A, et al. Phar-
macokinetic properties of radiolabeled mutant Interleukin-2v: a PET imaging study. Oncotarget
2018;9(6):7162–74.
[184] Di Gialleonardo V, Signore A, Glaudemans AW, Dierckx RA, De Vries EF. N-(4-18F-
fluorobenzoyl)interleukin-2 for PET of human-activated T lymphocytes. J Nucl Med 2012;53
(5):679–86.
[185] Markovic SN, Galli F, Suman VJ, Nevala WK, Paulsen AM, Hung JC, et al. Non-invasive visuali-
zation of tumor infiltrating lymphocytes in patients with metastatic melanoma undergoing immune
checkpoint inhibitor therapy: a pilot study. Oncotarget 2018;9(54):30268–78.
[186] Murer P, Neri D. Antibody-cytokine fusion proteins: a novel class of biopharmaceuticals for the ther-
apy of cancer and of chronic inflammation. New Biotechnol 2019;52:42–53.
[187] van Brummelen EMJ, Huisman MC, de Wit-van der Veen LJ, Nayak TK, Stokkel MPM, Mulder
ER, et al. (89)Zr-labeled CEA-targeted IL-2 variant immunocytokine in patients with solid tumors:
CEA-mediated tumor accumulation and role of IL-2 receptor-binding. Oncotarget 2018;9
(37):24737–49.
[188] Bots M, Medema JP. Granzymes at a glance. J Cell Sci 2006;119(24):5011–4.
[189] Larimer BM, Wehrenberg-Klee E, Dubois F, Mehta A, Kalomeris T, Flaherty K, et al. Granzyme
B PET imaging as a predictive biomarker of immunotherapy response. Cancer Res 2017;77
(9):2318–27.
[190] Oka S, Okudaira H, Ono M, Schuster DM, Goodman MM, Kawai K, et al. Differences in transport
mechanisms of trans-1-amino-3-[18F]fluorocyclobutanecarboxylic acid in inflammation, prostate
cancer, and glioma cells: comparison with L-[methyl-11C]methionine and 2-deoxy-2-[18F]
fluoro-D-glucose. Mol Imaging Biol 2014;16(3):322–9.
[191] Kanagawa M, Doi Y, Oka S, Kobayashi R, Nakata N, Toyama M, et al. Comparison of trans-1-
amino-3-[18F]fluorocyclobutanecarboxylic acid (anti-[18F]FACBC) accumulation in lymph node
prostate cancer metastasis and lymphadenitis in rats. Nucl Med Biol 2014;41(7):545–51.
[192] Labadie BW, Bao R, Luke JJ. Reimagining IDO pathway inhibition in cancer immunotherapy via
downstream focus on the tryptophan–kynurenine–aryl hydrocarbon axis. Clin Cancer Res 2019;25
(5):1462–71.
[193] Huang X, Pan Z, Doligalski ML, Xiao X, Ruiz E, Budzevich MM, et al. Evaluation of radio-
fluorinated carboximidamides as potential IDO-targeted PET tracers for cancer imaging. Oncotarget
2017;8(29):46900–14.
[194] Juha´sz C, Chugani DC, Muzik O, Wu D, Sloan AE, Barger G, et al. In vivo uptake and metabolism of
alpha-[11C]methyl-L-tryptophan in human brain tumors. J Cereb Blood Flow Metab 2006;26
(3):345–57.
[195] Juha´sz C, Muzik O, Lu X, Jahania MS, Soubani AO, Khalaf M, et al. Quantification of tryptophan
transport and metabolism in lung tumors using PET. J Nucl Med 2009;50(3):356–63.
[196] Juha´sz C, Nahleh Z, Zitron I, Chugani DC, Janabi MZ, Bandyopadhyay S, et al. Tryptophan metab-
olism in breast cancers: molecular imaging and immunohistochemistry studies. Nucl Med Biol
2012;39(7):926–32.
[197] Zitron IM, Kamson DO, Kiousis S, Juha´sz C, Mittal S. In vivo metabolism of tryptophan in menin-
giomas is mediated by indoleamine 2,3-dioxygenase 1. Cancer Biol Ther 2013;14(4):333–9.
[198] Alkonyi B, Barger GR, Mittal S, Muzik O, Chugani DC, Bahl G, et al. Accurate differentiation of
recurrent gliomas from radiation injury by kinetic analysis of α-11C-methyl-L-tryptophan PET.
J Nucl Med 2012;53(7):1058–64.
[199] Radu CG, Shu CJ, Nair-Gill E, Shelly SM, Barrio JR, Satyamurthy N, et al. Molecular imaging of
lymphoid organs and immune activation by positron emission tomography with a new [18F]-labeled
2’-deoxycytidine analog. Nat Med 2008;14(7):783–8.
[200] Kim W, Le TM, Wei L, Poddar S, Bazzy J, Wang X, et al. [18F]CFA as a clinically translatable probe
for PET imaging of deoxycytidine kinase activity. Proc Natl Acad Sci U S A 2016;113(15):4027–32.
[201] Ronald JA, Kim B-S, Gowrishankar G, Namavari M, Alam IS, D’Souza A, et al. A PET imaging
strategy to visualize activated T cells in acute graft-versus-host disease elicited by allogenic hemato-
poietic cell transplant. Cancer Res 2017;77(11):2893–902.
[202] Franc BL, Goth S, MacKenzie J, Li X, Blecha J, Lam T, et al. In vivo PET imaging of the activated
immune environment in a small animal model of inflammatory arthritis. Mol Imaging 2017;16.
1536012117712638.
[203] Levi J, Lam T, Goth SR, Yaghoubi S, Bates J, Ren G, et al. Imaging of activated T cells as an early
predictor of immune response to anti-PD-1 therapy. Cancer Res 2019;79(13):3455– 65.
465Image-guided cancer immunotherapy
466 Thomas S.C. Ng and Miles A. Miller
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
[204] Buck MD, O’Sullivan D, Pearce EL. T cell metabolism drives immunity. J Exp Med 2015;212
(9):1345–60.
[205] Depil S, Duchateau P, Grupp SA, Mufti G, Poirot L. ‘Off-the-shelf’ allogeneic CAR T cells: devel-
opment and challenges. Nat Rev Drug Discov 2020;19(3):185–99.
[206] Minn I, Rowe SP, Pomper MG. Enhancing CAR T-cell therapy through cellular imaging and radio-
therapy. Lancet Oncol 2019;20(8):e443–51.
[207] Grimfors G, Schnell PO, Holm G, Johansson B, Mellstedt H, Pihlstedt P, et al. Tumour imaging of
indium-111 oxine-labelled autologous lymphocytes as a staging method in Hodgkin’s disease. Eur
J Haematol 1989;42(3):276–83.
[208] Fisher B, Packard BS, Read EJ, Carrasquillo JA, Carter CS, Topalian SL, et al. Tumor localization of
adoptively transferred indium-111 labeled tumor infiltrating lymphocytes in patients with metastatic
melanoma. J Clin Oncol 1989;7(2):250–61.
[209] Griffith KD, Read EJ, Carrasquillo JA, Carter CS, Yang JC, Fisher B, et al. In vivo distribution of
adoptively transferred indium-111-labeled tumor infiltrating lymphocytes and peripheral blood lymphocytes in patients with metastatic melanoma. J Natl Cancer Inst 1989;81(22):1709–17.
[210] Pittet MJ, Grimm J, Berger CR, Tamura T, Wojtkiewicz G, Nahrendorf M, et al. In vivo imaging of
T cell delivery to tumors after adoptive transfer therapy. Proc Natl Acad Sci U S A 2007;104
(30):12457–61.
[211] Stanton SE, Eary JF, Marzbani EA, Mankoff D, Salazar LG, Higgins D, et al. Concurrent SPECT/
PET-CT imaging as a method for tracking adoptively transferred T-cells in vivo. J Immunother Cancer 2016;4:27.
[212] Man F, Lim L, Volpe A, Gabizon A, Shmeeda H, Draper B, et al. In vivo PET tracking of 89Zr-
labeled Vγ9Vδ2 T cells to mouse xenograft breast tumors activated with liposomal alendronate.
Mol Ther 2019;27(1):219–29.
[213] Kircher MF, Allport JR, Graves EE, Love V, Josephson L, Lichtman AH, et al. In vivo high resolution
three-dimensional imaging of antigen-specific cytotoxic T-lymphocyte trafficking to tumors. Cancer
Res 2003;63(20):6838–46.
[214] Chapelin F, Capitini CM, Ahrens ET. Fluorine-19 MRI for detection and quantification of immune
cell therapy for cancer. J Immunother Cancer 2018;6(1):105.
[215] Gonzales C, Yoshihara HAI, Dilek N, Leignadier J, Irving M, Mieville P, et al. In-vivo detection and
tracking of T cells in various organs in a melanoma tumor model by 19F-fluorine MRS/MRI. PLoS
One 2016;11(10), e0164557.
[216] Moroz MA, Zhang H, Lee J, Moroz E, Zurita J, Shenker L, et al. Comparative analysis of T cell imag-
ing with human nuclear reporter genes. J Nucl Med 2015;56(7):1055–60.
[217] Krebs S, Ahad A, Carter LM, Eyquem J, Brand C, Bell M, et al. Antibody with infinite affinity for in
vivo tracking of genetically engineered lymphocytes. 2018;59(12):1894–900.
[218] Vedvyas Y, Shevlin E, Zaman M, Min IM, Amor-Coarasa A, Park S, et al. Longitudinal PET imaging
demonstrates biphasic CAR T cell responses in survivors. JCI insight 2016;1(19), e90064.
[219] Minn I, Huss DJ, Ahn HH, Chinn TM, Park A, Jones J, et al. Imaging CAR T cell therapy with
PSMA-targeted positron emission tomography. Sci Adv 2019;5(7), eaaw5096.
[220] Sellmyer MA, Richman SA, Lohith K, Hou C, Weng C-C, Mach RH, et al. Imaging CAR T cell
trafficking with eDHFR as a PET reporter gene. Mol Ther 2020;28(1):42–51.
[221] Yaghoubi SS, Jensen MC, Satyamurthy N, Budhiraja S, Paik D, Czernin J, et al. Noninvasive detec-
tion of therapeutic cytolytic T cells with 18F-FHBG PET in a patient with glioma. Nat Clin Pract
Oncol 2009;6(1):53–8.
[222] Keu KV, Witney TH, Yaghoubi S, Rosenberg J, Kurien A, Magnusson R, et al. Reporter gene imag-
ing of targeted T cell immunotherapy in recurrent glioma. Sci Transl Med 2017;9(373), eaag2196.
[223] Ponomarev V, Doubrovin M, Lyddane C, Beresten T, Balatoni J, Bornman W, et al. Imaging TCR-
dependent NFAT-mediated T-cell activation with positron emission tomography in vivo. Neoplasia
2001;3(6):480–8.
[224] Uchibori R, Teruya T, Ido H, Ohmine K, Sehara Y, Urabe M, et al. Functional analysis of an induc-
ible promoter driven by activation signals from a chimeric antigen receptor. Mol Ther Oncolytics
2019;12:16–25.
[225] Goebeler M-E, Bargou RC. T cell-engaging therapies—BiTEs and beyond. Nat Rev Clin Oncol
2020;17(7):418–34.
[226] Moek KL, Waaijer SJH, Kok IC, Suurs FV, Brouwers AH. Menke-van der Houven van Oordt CW,
et al. (89)Zr-labeled Bispecific T-cell Engager AMG 211 PET Shows AMG 211 accumulation in
CD3-rich tissues and clear, heterogeneous tumor uptake. Clin Cancer Res 2019;25(12):3517–27.
[227] Suurs FV, Lorenczewski G, Stienen S, Friedrich M, de Vries EGE, De Groot DJA, et al. Bio-
distribution of a CD3/EpCAM bispecific T-cell engager is driven by the CD3 arm. J Nucl Med 2020.
[228] Kim DY, Han KH. Transarterial chemoembolization versus transarterial radioembolization in hepa-
tocellular carcinoma: optimization of selecting treatment modality. Hepatol Int 2016;10(6):883–92.
[229] Visioni A, Kim M, Wilfong C, Blum A, Powers C, Fisher D, et al. Intra-arterial versus intravenous
adoptive cell therapy in a mouse tumor model. J Immunother 2018;41(7):313–8.
[230] Sheth RA, Murthy R, Hong DS, Patel S, Overman MJ, Diab A, et al. Assessment of image-guided
intratumoral delivery of immunotherapeutics in patients with cancer. JAMA Netw Open 2020;3(7),
e207911.
[231] Erinjeri JP, Fine GC, Adema GJ, Ahmed M, Chapiro J, den Brok M, et al. Immunotherapy and the
interventional oncologist: challenges and opportunities—a society of interventional oncology white
paper. Radiology 2019;292(1):25–34.
[232] Mizukoshi E, Nakamoto Y, Arai K, Yamashita T, Sakai A, Sakai Y, et al. Comparative analysis of
various tumor-associated antigen-specific t-cell responses in patients with hepatocellular carcinoma.
Hepatology 2011;53(4):1206–16.
[233] Lovitch SB, Rodig SJ. The role of surgical pathology in guiding cancer immunotherapy. Annu Rev
Pathol Mech Dis 2016;11(1):313–41.
[234] Giraldo NA, Becht E, Vano Y, Saute`s-Fridman C, Fridman WH. The immune response in cancer:
from immunology to pathology to immunotherapy. Virchows Arch 2015;467(2):127–35.
[235] Quandt D, Dieter Zucht H, Amann A, Wulf-Goldenberg A, Borrebaeck C, Cannarile M, et al.
Implementing liquid biopsies into clinical decision making for cancer immunotherapy. Oncotarget
2017;8(29):48507–20.
[236] Tie J. Tailoring immunotherapy with liquid biopsy. Nat Cancer 2020;1(9):857–9.
467Image-guided cancer immunotherapy
Further reading
Momcilovic M, Shackelford DB. Imaging cancer metabolism. Biomol Ther (Seoul) 2018;26(1):81–92.
Widmann G, Nguyen VA, Plaickner J, Jaschke W. Imaging features of toxicities by immune checkpoint
inhibitors in cancer therapy. Curr Radiol Rep 2016;5(11):59.
CHAPTER FOURTEEN
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
Clinical translation and challenges
in cancer immunotherapies
Amit Singh
Intergalactic Therapeutics, Boston, MA, United States
Contents
1. Introduction 469
2. Challenges in developing cancer immunotherapies 470
2.1 Development of relevant preclinical models 470
2.2 Identifying dominant drivers of cancer immunity 472
2.3 Understanding organ-specific TME 473
2.4 Understanding drivers of immune evasion 474
2.5 Harvesting endogenous vs synthetic immunity 475
2.6 Endpoint assessment and data integration 476
2.7 Characterization of autoimmunity and anticancer immunity 477
2.8 Maximize personalized therapeutic approach 478
2.9 Improved regulatory endpoints 479
2.10 Optimize survival through combination therapy 480
3. Drug development considerations for clinical translation 482
3.1 Early-phase considerations 483
3.2 Late-phase considerations 486
4. Conclusion 487
References 488
1. Introduction
The Nobel prize for physiology and medicine in 2018 was awarded to James P.
Allison and Tasuko Honjo for their work showing how proteins on the surface of
immune cells can be used to reinstate their antitumor response. Their approach since then
has led to the development of several anticancer immunotherapies that have significantly
improved and extended the median survival rate of patients by years and in some cases
resulted in complete recovery. The past decade has seen tremendous growth in the successful clinical development of such cancer immunotherapies and their approval as drugs
that significantly improve the quality of life and overall survival of terminal patients. The
success of this approach across different forms of cancers is a testament to the importance
Engineering Technologies and Clinical Translation Copyright © 2022 Elsevier Inc.
All rights reserved.https://doi.org/10.1016/B978-0-323-90949-5.00014-0
469
470 Amit Singh
of immune system interaction with tumor cells. However, this approach has shown benefit in only some cancer types and treatment has only been effective in a small minority of
patient subsets suggesting the complexity in the development of successful cancer immunotherapies. The immune system is one of the most diverse and highly regulated systems
and cancer as a disease is extremely complex, heterogeneous, and exhibits tremendous
adaptability and resilience. Therefore, a positive outcome for cancer immunotherapy
would require multiple biochemical steps to be fulfilled sequentially, which is further
challenged by the ability of the cancer cells to evade and subvert immune recognition
through multiple different pathways [1].
2. Challenges in developing cancer immunotherapies
Cancer is a very dynamic disease and intratumoral and intertumoral diversity at the
cellular and molecular level is well acknowledged and documented. Same cancer presents
different cellular diversity, molecular profile, and physiological microenvironment from
one patient to another and even within the same patient, cancer can have different clonal
populations depending on the location of the tumor [2]. Tumor heterogeneity has serious
implications in designing a therapy against cancer, a disease that shows tremendous resilience and capability to adapt [3]. The task becomes even more challenging when the
approach involves recruiting the host immune system to mount a therapeutic response
against the disease. Despite tremendous development in the last decade in developing
cancer immunotherapies, there are significant challenges that have to be overcome to
achieve a robust and durable therapeutic response by overcoming all the variability associated with the disease. Hegde and Chen provided a very insightful perspective on 10 key
challenges in developing cancer immunotherapy that has been summarized in the subsequent sections [4].
2.1 Development of relevant preclinical models
In vitro and ex vivo disease, models have poor correlation with the complexities of the
actual disease and therefore are not very useful in facilitating clinical translation of a therapy. Drug discovery therefore heavily relies on preclinical animal models that can mimic
the real disease microenvironment and presents a high and accurate predictive value of
the clinical outcome of any therapy. In vivo studies help in the selection of the suitable
drug targets, understanding the mechanism of action of a drug, dose optimization, delivery strategy and toxicity, safety, and efficacy assessment. Therefore, the closer the tested
animal model is in reflecting the true nature of the disease at the molecular, cellular, and
physiological level, the higher is the probability of clinical success of a drug that is successful in preclinical studies. Unfortunately, the diversity and complexity of cancer are
seldom recapitulated in the preclinical models that are often used for preclinical screening. Some of the key differences from an immunotherapy standpoint include lack of
correlation in biomarker and tumor antigen expression levels, variability in the cellular
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composition of the tumor (including types and population of immune cells), and mechanisms involved in immune evasion (or the lack of it).
The majority of preclinical animal models used in developing anticancer therapy are
generated by implanting cancer cell lines grown in a 2-D culture either subcutaneously or
orthotopic xenografts. There is no control on the phenotypic and genotypic characterization of these cell lines and the subsequent tumor obtained from these cells is merely a
mass of cells with poor immune components and other subtitles that are inherent to the
actual tumor microenvironment (TME). Therefore, these cell-derived xenografts
(CDX) models have miserably failed in accurately predicting the efficacy of the therapeutic approach. Recent advancement in the field cancer biology and our increased understanding of the disease has also led to design and development of better preclinical models
that correlate more closely to the actual tumor. Genetically engineered mouse (GEM)
models were developed as a substitute by knocking out a tumor-suppressor gene or
inducing organ-specific somatic mutations using Cre-LoxP system resulting in tumorigenesis. These models represent the natural microenvironment of organ-specific tumors
but unlike real tumors that result from the accumulation of genetic mutations over a long
period of time leading to disease, tumors from GEM models are genetically stable and do
not have similar immune complexity. More importantly, these cancer models lack the
molecular and biological signatures of the real human tumor including suitable biomarker
expression that may be critical to the clinical outcome [5].
In this regard, patient-derived xenograft (PDX) models where clinical tumor samples
are implanted subcutaneously in the immune-compromised mice have a close resemblance to the human tumor since the 3-D tumor architecture and cellular complexity
is directly mirrored. The PDX model carries all the cellular and molecular signatures from
the patient and, therefore, serves as the best model for patient-specific “personalized and
targeted” therapy development. There has been a rapid increase in the PDX model for
drug discovery due to increased access to the clinical tumor samples, better preservation,
and transportation technology, and markedly higher correlation to the clinical outcome
in patient. However, these models still may not provide an accurate representation of the
immune system-cancer cell interplay since the host lacks a functional immune system and
so they are not particularly suitable for the development of cancer immunotherapy.
Humanized PDX has been looked at as an alternative where either human immune component such as macrophages, NK cells, T cells, B cells, etc., are introduced into the
immune-compromised mice along with the tumor tissue; tumor containing stromal
and immune components or replacement of complete hematopoietic system (and thus
replacing the adaptive and innate immunity) [6]. A humanized mouse model generated
by hematopoietic replacement has been successfully used to characterize the immune
response to anti-PD-1 mono and combination therapy in triple-negative breast cancer
cell line and colorectal PDX model [7]. The development and selection of clinically
471Clinical translation and challenges in cancer immunotherapies
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