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Biopolymer implants enhance the efficacy of adoptive T cell therapy
research-article
Author(s):
Sirkka B. Stephan
1 ,
Alexandria M. Taber
1 ,
Ilona Jileaeva
2 ,
Ericka P. Pegues
2 ,
Charles L. Sentman
3 ,
Matthias T. Stephan
1
,
2
,
4
,
5
Publication date (Electronic):
15 December 2014
Journal:
Nature biotechnology
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Although adoptive T cell therapy holds promise for the treatment of many cancers, its clinical utility has been limited by problems in delivering targeted lymphocytes to tumor sites, and their inefficient expansion in the immunosuppressive tumor microenvironment. Here we describe a bioactive polymer implant capable of delivering, expanding and dispersing tumor-reactive T cells. The approach can be used to treat inoperable or incompletely-removed tumors by situating implants near them, or at resection sites. Using a mouse breast cancer resection model, we show that the implants effectively support tumor-targeting T cells throughout resection beds and associated lymph nodes, and reduce tumor relapse compared to conventional delivery modalities. In a multifocal ovarian cancer model, we demonstrate that polymer-delivered T cells trigger regression whereas injected tumor-reactive lymphocytes have little curative effect. Scaffold-based T cell delivery may provide a viable treatment option for inoperable tumors, and reduce the rate of metastatic relapse after surgery.
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Most cited references24
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Mesoporous silica nanoparticles in medicine--recent advances.
Veronika Mamaeva, Cecilia Sahlgren, Mika Linden (2013)
MSNs have attracted increasing interest as drug carriers due to promising in vivo results in small-animal disease models, especially related to cancer therapy. In most cases small hydrophobic drugs have been used, but recent in vitro studies demonstrate that MSNs are highly interesting for gene delivery applications. This review covers recent advances related to the therapeutic use of mesoporous silica nanoparticles (MSNs) administered intravenously, intraperitoneally or locally. We also cover the use of MSNs in alternative modes of therapy such as photodynamic therapy and multidrug therapy. We further discuss the current understanding about the biodistribution and safety of MSNs. Finally, we critically discuss burning questions especially related to experimental design of in vivo studies in order to enable a fast transition to clinical trials of this promising drug delivery platform. Copyright © 2012 Elsevier B.V. All rights reserved.
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Controlling alginate gel degradation utilizing partial oxidation and bimodal molecular weight distribution.
Degradability is often a critical property of materials utilized in tissue engineering. Although alginate, a naturally derived polysaccharide, is an attractive material due to its biocompatibility and ability to form hydrogels, its slow and uncontrollable degradation can be an undesirable feature. In this study, we characterized gels formed using a combination of partial oxidation of polymer chains and a bimodal molecular weight distribution of polymer. Specifically, alginates were partially oxidized to a theoretical extent of 1% with sodium periodate, which created acetal groups susceptible to hydrolysis. The ratio of low MW to high MW alginates used to form gels was also varied, while maintaining the gel forming ability of the polymer. The rate of degradation was found to be controlled by both the oxidation and the ratio of high to low MW alginates, as monitored by the reduction of mechanical properties and corresponding number of crosslinks, dry weight loss, and molecular weight decrease. It was subsequently examined whether these modifications would lead to reduced biocompatibility by culturing C2C12 myoblast on these gels. Myoblasts adhered, proliferated, and differentiated on the modified gels at a comparable rate as those cultured on the unmodified gels. Altogether, this data indicates these hydrogels exhibit tunable degradation rates and provide a powerful material system for tissue engineering.
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Ovarian cancer progression is controlled by phenotypic changes in dendritic cells
Epithelial ovarian cancer is a devastating disease responsible for the deaths of ∼15,000 Americans per year, even more than melanoma or brain tumors (Jemal et al., 2009). Independent studies have demonstrated that in the ovarian carcinoma microenvironment, T cells (and only they) can spontaneously exert clinically relevant pressure against tumor progression (Zhang et al., 2003; Sato et al., 2005; Hamanishi et al., 2007). However, as the dismal statistics show, immune pressure against established tumors is insufficient. In part, this is because when tumors become clinically symptomatic, they have already overcome the immune system through multiple complementary mechanisms. The “cancer immunoediting” hypothesis, supported by recent experimental and clinical evidence, provides a frame to understand this process (Schreiber et al., 2011). The model implies that all symptomatic tumors represent a failure of the immune system. Recent studies have postulated that tumors can be kept in check for long periods, through a dynamic balance that results in the progressive loss of immunogenicity by tumor cells. However, emerging clinical evidence from multiple trials blocking common immunosuppressive checkpoints (such as CTLA4 or PD-1) indicates that preventing tumor-induced T cell paralysis restores protective immunity against established cancers, implying that advanced tumors remain somewhat immunogenic. Based on multiple lines of evidence, the model has recently evolved to include the role of immunosuppression in the tumor microenvironment in this process. However, the relative contribution of individual microenvironmental populations to suppress or support the capacity of tumors to expand and their dynamics remains unclear. Some excellent non-transplantable models of ovarian cancer are available, but mutagenic events activated during embryonic development result in animals that are born with premalignant lesions (Connolly et al., 2003; Xing et al., 2009), which complicates their use for understanding tumor initiation. For instance, seminal studies by Clark et al. (2007), using a genetic model of pancreatic cancer, found immune tolerance against cancer soon after birth. This may reflect the distinctive physiopathology of pancreatic cancer but also be the result of defective immunosurveillance when mutations are initiated before the development of a mature immune system. To understand how the evolution of the inflammatory microenvironment of developing aggressive ovarian cancers influences tumor progression, we have generated a new p53-dependent model that recapitulates the immune populations of human tumors in previously healthy hosts. Our results show that accelerated malignant progression after a relatively long period of immune control is triggered by a phenotypic switch in expanding DC infiltrates, which can be reversed upon DC depletion, without specifically ablating tumor cells. RESULTS Generation of a p53-dependent inducible metastatic ovarian carcinoma To model the immunobiology of aggressive (type II) ovarian cancers (Kurman and Shih, 2011), we first sought to generate an inducible cancer model in previously healthy adult mice, avoiding the initiation of carcinogenic events before the development of a mature immune system. For that purpose, we used a previously described technique (Flesken-Nikitin et al., 2003; Dinulescu et al., 2005), based on the delivery of recombinant adenoviruses expressing Cre recombinase into the ovarian bursal cavity. Ablation of only p53—the hallmark of malignancy in human ovarian carcinoma (Bernardini et al., 2010)—did not result in any obvious carcinogenic event >200 d after induction of the mutation (not depicted). To add a relevant second mutagenic event, we investigated the occurrence of KRAS deregulation in a cohort of 60 unselected stage III–IV human ovarian carcinoma specimens. We found highly variable levels of KRAS mRNA in both metastatic and primary specimens, but all were higher than the very low levels of an immortalized ovarian surface epithelial (IOSE) cell line (Wang et al., 2006; Fig. 1 A). Most importantly, KRAS protein was also dramatically overexpressed in multiple tumors, compared with IOSE cells or HOSEpiC epithelial cells from healthy ovaries cryopreserved either at primary or passage one cultures (Fig. 1 B), indicating that, beyond the frequent gene amplification recently reported by the Cancer Genome Atlas Network (Bell et al., 2011), KRAS is deregulated in most advanced human ovarian cancers. Figure 1. The p53-dependent mouse model of ovarian carcinoma develops solid peritoneal tumor masses. (A) Expression of KRAS mRNA (normalized to GAPDH) in 17 representative cases of stage III–IV human ovarian carcinomas, relative to IOSE cells (immortalized cells from the normal ovarian epithelial surface). (B) Relative protein expression of K-ras (normalized to β-actin) in seven randomly selected stage III–IV human ovarian carcinoma specimens, IOSE cells, or HOSEpiC cells (healthy ovarian epithelial cells cryopreserved either at primary or passage one cultures). (C) Injection of trypan blue into the bursal cavity of transgenic mice. (D) Intrabursal adenoviral injection in p53/K-ras mice results in a primary solid ovarian tumor mass (P) with accompanying metastatic lesion (M). U, uterus. (E) Ascites detected in the peritoneal cavity of a chimeric p53/K-ras mouse injected with the adenovirus ∼50 d before. (F) Primary (P) and spontaneous metastatic masses (white arrows) in p53/K-ras mice 50 d after receiving adenovirus. (G) H&E of metastatic masses from indicated regions which appeared in mice 3 mo after undergoing resection of primary tumors at ∼35 d. Black arrows indicate tumor tissue. Bars, 100 µm. (H) p53/K-ras primary epithelial tumors stained for Cytokeratin 8 (CK8; ×100), Pan-Cytokeratin (PanCK; ×200), smooth muscle actin (SMA; ×100); and Vimentin (×100). Bars, 100 µm. Brightness, contrast, and color balance were uniformly adjusted in whole individual images. To model the constitutive activation of KRAS, we took advantage of existing LSL-K-rasG12D/+ mice (Jackson et al., 2001). Intrabursal delivery makes injected materials accessible to both the epithelial surface and the fimbriated epithelium at the interphase with the oviduct (Fig. 1 C). Concurrent ablation of p53 and activation of oncogenic K-ras in double (p53/K-ras) transgenic mice then resulted in palpable abdominal tumor lesions with 100% penetrance only 35 d after adenoviral administration. Tumors were found to be perfectly solid as they became more advanced (Fig. 1 D). In a proportion of animals we detected gross hemorrhagic ascites (Fig. 1 E), as well as metastases at multiple peritoneal locations (Fig. 1, F and G). Histological sections of the primary and metastatic tumor sites revealed a growth pattern characterized by solid masses comprised of malignant spindled cells with interspersed large anaplastic tumor cells. The neoplastic cells were immunoreactive for cytokeratin 8, desmin (not depicted), and smooth muscle actin and were negative for vimentin (Fig. 1 H). Patchy immunoreactivity with a pan-cytokeratin antibody cocktail was also noted (Fig. 1 H). Although on a morphological basis, sarcoma is a consideration, the immunohistochemical staining pattern is most consistent with sarcomatoid ovarian carcinoma (i.e., induction of a high-grade poorly differentiated ovarian carcinoma with spindle cell morphology). Together, these data indicate that concurrent p53 and K-ras mutations in the epithelial ovarian surface and/or fimbriated epithelium at the interphase with the oviduct result in highly metastatic tumors with complete penetrance and short latency. Inducible ovarian tumors recapitulate the inflammatory microenvironment of human ovarian cancer Nonreplicating adenoviral-Cre delivery in WT mice altered the leukocytic ovarian microenvironment only in a temporary manner, as the proportions of tissue-resident CD11c+ DCs were unchanged between 1 and 5 wk after surgical manipulation (Fig. 2 A). Ovarian T cell infiltration (the other predominant leukocyte subset in healthy ovaries) was similarly unaffected (Fig. 2 A). However, when we performed an exhaustive phenotypic analysis of the immune microenvironment of solid advanced p53/K-ras tumor masses, we found a relatively homogeneous population of CD11c+DEC205+MHC-II+CD11b+CD86−CD40low DCs, supporting our previous observations in transplantable models (Scarlett et al., 2009; Fig. 2, B and C). These populations of DCs expressing low levels of co-stimulatory molecules were also present in tumor draining LNs (DLNs) of animals with end stage tumors (Fig. 2 B). Most importantly, terminal tumors recapitulated the immune microenvironment of advanced human ovarian carcinomas, including a heterogeneous mix of antigen-presenting cells (Wilke et al., 2011). Thus, in advanced solid tumors of humans and p53/K-ras mice, the predominant leukocyte subset expressed phenotypic markers of bona fide DCs, including CD11c, DEC205, and MHC-II (Fig. 2, C and D). In humans, the precise categorization of solid tumor-infiltrating myeloid leukocytes was more complicated as the result of a high degree of phenotypic overlap between DCs and macrophages in some specimens. However, the most abundant leukocyte in at least a third of the specimens analyzed clearly lacked the monocyte/macrophage markers CD11b or CD14 but coexpressed the DC marker CD11c (Fig. 2, E–G; and Table 1). These cells also maintain a high level of expression of MHC-II, so they cannot be alternatively defined as more immature myeloid-derived suppressor cells (Nagaraj and Gabrilovich, 2010; Fig. 2 H and Table 1). Interestingly, DCs in our mouse model progressively down-regulated MHC-II expression until ∼35 d, whereas DCs found within advanced tumors (>50 d) showed intermediate MHC-II expression. In contrast, the microenvironment of ascites showed the presence of predominant bona fide macrophages (Fig. 2 G). Therefore, the distinctive product of excessive myelopoiesis in most human ovarian cancer solid tumor masses is a leukocyte with predominant phenotypic attributes of DCs, rather than canonical macrophages, which is recapitulated in our animal model. Figure 2. The inflammatory microenvironment of p53/K-ras end-stage tumors recapitulates advanced human ovarian carcinoma. (A) Quantification at the indicated time points of cells found within the ovaries of WT animals (n = 8) after receiving adenovirus intrabursally. Gated on CD45+ cells. (B) Expression of indicated activation markers on CD45+CD11c+ DCs taken from tumors or DLN of p53/K-ras mice. Early, 7 d after AdvCre injection; Advanced, mice with advanced tumors. Representative density plots of dissociated tumors from p53/K-ras mice (C) and patients with stage III–IV ovarian carcinoma (gated on CD45; D). (E–G) Density flow plots of dissociated tumors from individual patients with stage III–IV ovarian carcinoma. (H) Gating strategy (above) and isotypes (below) for Table 1. Error bars, SEM. Table 1. Flow analysis of leukocytes infiltrating solid Stage III/IV human ovarian tumors Patients Gated on CD45 Gated on CD11c+HLA-DR+ Gated on Dec205− Gated on CD45 CD11c+ CD11c+HLA-DR+ Dec205+ Dec205− CD11b+CD14+ CD20+ CD3+ S1 Patient 1 52 30.9 39.9 61.7 28.6 1.78 30.9 S2 Patient 2 47.2 47.1 59.1 39.4 29.2 2.79 45.7 S4 Patient 3 (P) 84.7 82.2 17.2 81.2 4.9 n/a n/a S5 Patient 3 (M) 84.8 56.3 12 76.8 15.6 n/a n/a S6 Patient 4 12 12.3 70.3 30.1 44.5 n/a n/a S7 Patient 5 57.1 56.5 70.3 25.8 21.5 n/a n/a S8 Patient 6 52.8 52.4 41.3 59.9 7.04 1.78 30.9 S9 Patient 7 41.7 40 n/a n/a n/a 0.27 10.2 S10 Patient 8 28.2 6.35 n/a n/a n/a 0.013 0.49 S11 Patient 9 37.2 36.5 n/a n/a n/a 2.85 8.34 S12 Patient 10 37.1 5.4 n/a n/a n/a 0.045 1.92 Gating strategy is outlined in Fig. 2 H. n/a, insufficient material. Values are expressed as percentages. Besides corresponding expression of phenotypic determinants, CD45+CD11c+MHC-II+ DCs sorted from dissociated p53/K-ras advanced tumors and CD45+CD11chiHLA-DRhiDec205int from unselected human tumor specimens responded to PMA and ionomycin by secreting a comparable pattern of proinflammatory chemokines. Those included high levels of CCL3 and CCL4, in addition to pro-angiogenic IL-8/KC (Fig. 3 A). A similar cytokine profile was found in DCs sorted from a transplantable mouse model of ovarian cancer (ID8-Debf29/Vegf-A; Fig. 3 A). Together, these data indicate that our p53-dependent tumor model faithfully recapitulates the immune microenvironment of human ovarian cancer, and it is therefore suitable to understand its unknown dynamics. Figure 3. DCs found within solid p53/K-ras tumors display similar proinflammatory attributes as human tumor-derived DCs. (A) Quantification of cytokines and chemokines secreted from sorted CD45+CD11c+Dec205+CD11b+/− cells from advanced human tumor specimens (n = 3) or CD45+CD11c+CD11b+ cells from tumors of end-stage p53/K-ras mice (n = 4) or ascites of WT mice bearing ID8-Defb29/Vegf-a tumors. (B) Proportion of CD45+ cells found within the ovaries of p53/K-ras mice (n = 6) at the indicated time points after adenovirus injection. (C) CD45 immunohistochemical analysis of resected tumors (>50 d) ×100. Error bars, SEM. Bar, 100 µm. Accelerated tumor growth after prolonged stability coincides with a switch in the inflammatory infiltrate We found that, starting after day 21 after the adenovirus-Cre challenge, tumors accumulated progressively denser immune cell infiltrates (Fig. 3, B and C). Remarkably, tumor progressed through a prolonged equilibrium phase for ∼28 d, when no obvious macroscopic masses were detectable (Fig. 4 A, top). Leukocytes other than DCs in tumor-developing ovaries at this stage did not significantly differ from surgically treated ovaries in WT mice, and they were characterized by predominant T cell infiltrates after day 7 (Fig. 4, A [bottom] and B). Figure 4. A change in the inflammatory microenvironment converges with the exponential growth of p53/K-ras tumors. (A) Top, images of explanted reproductive organs from p53/K-ras mice after receiving adenovirus. Middle and bottom, quantified flow analysis of resected tumors (red circle; primordial and advanced) from p53/K-ras (middle; n = 4–6) and WT (bottom; n = 6–8) animals, which received adenovirus previously. Slices represent mean percentages of CD45+ leukocytic infiltrates. (B) Comparative analysis of CD45+CD11c+CD11b− and CD45+CD3+ cells infiltrating tumors of p53/K-ras mice at the indicated time points (n = 4–8). (C) Density plots of p53/K-ras tumors or WT ovaries which received adenovirus 35 d before (gated on CD45+ cells). Histogram of CD45+F4/80+CD11c− (blue) and CD45+CD11c+F4/80− (red). Numbers in the right corner are respective mean fluorescence intensity (MFI). (D) Percentage of CD45+CD11c+ and CD45+CD3+ cells found within stage III/IV human ovarian carcinoma (n = 7). (E and F) Quantification of flow analysis at the indicated time points of ovaries from p53/K-ras (n = 4–6) or WT (n = 6–8) mice after adenovirus injection. Error bars, SEM. *, P 60 d) specimens. However, T cells sorted from the DLNs of advanced (>50 d) tumor-bearing mice produced significantly fewer IFN-γ spots in response to the same antigens (Fig. 5, D and E, left). T cell unresponsiveness at advanced stages was further confirmed by diminished proliferative responses in response to the same tumor antigens (Fig. 5 E, middle and right). Because DCs pulsed with cells derived from advanced tumors induced significant proliferative responses in early tumor-associated T cells, these data demonstrate that tumor-specific T cells become intrinsically less responsive during advanced malignant progression. Therefore, T cell–dependent tumor-specific immune responses are elicited from a very early stage after tumor initiation, but they are abrogated during the course of the disease. Figure 5. Reduced adaptive immunity during accelerated tumor growth. (A) Representative images of differential tumor burden (primary plus metastatic growth) in mice challenged with intrabursal adenovirus-Cre that received depleting anti-CD8 (α-CD8) versus isotype control (iIgG) antibodies at days −2, 5, 12, and 19 (n = 4/group). (B) Left, ELISPOT analysis of IFN-γ produced by FACS-sorted CD45+CD11b−CD11c−SSClowCD8β +/CD4+ T cell splenocytes incubated with tumor pulsed BMDCs (10:1). Spleens are from either WT or p53/K-ras (early) animals that received adenovirus-Cre intrabursally 7 d before (n = 5 mice/group; two independent experiments). Middle, quantified proliferation of sorted T cell splenocytes from early tumor-bearing p53/K-ras or WT animals in response to BMDC-presented tumor antigens (n ≥ 5 mice/group; two independent experiments). Right, representative histogram of CFSE dilution in this experiment. (C) Left, ELISPOT analysis of Granzyme B (GZB) produced by CD45+CD11b−CD11c−SSClowCD8β +/CD4+ T cells FACS sorted from DLNs (renal) from the same mice, after incubation with tumor pulsed BMDCs (10:1). Middle, quantified proliferation of these lymphatic T cells in response to BMDC-presented tumor antigens. (D) Percentage of IFN-γ spots produced by CD45+CD11b−CD11c−SSClowCD8β +/CD4+ T cells sorted from the DLN of either WT mice that received adenovirus 7 d before, or p53/K-ras advanced tumor-bearing mice. Normalized to the number of spots produced by sorted DLN T cells from day 7 (early) tumor-bearing mice, which is considered 100% (n ≥ 4 mice/group; pooled from two independent experiments). (E) Left, ELISPOT analysis of IFN-γ produced by CD45+CD11b−CD11c−SSClowCD8β +/CD4+ T cells sorted from the DLN of p53/K-ras animals that received adenovirus-Cre intrabursally either 7 d (early) or >50 d (advanced) before. Middle, quantified proliferation of sorted T cells from the same mice in response to BMDC-presented tumor antigens (n = 3 mice/group; three independent experiments, total). Right, representative histogram of CFSE dilution in this experiment. Error bars, SEM. *, P 7 mo to advance even after the addition of a second mutation, different from KRAS (Flesken-Nikitin et al., 2003). Overall, our results suggest that even if primordial tumor lesions can be established, tumor microenvironmental leukocytes prevent their unrelenting growth for a relatively prolonged equilibrium phase. Only when the inflammatory infiltrate undergoes precise phenotypic and quantitative changes does tumor growth become exponential and thus clinically apparent. MATERIALS AND METHODS Animals and tissues. WT C57BL/6 mice were procured from the National Cancer Institute or The Jackson Laboratory, under Institutional Animal Care and Use Committee approval. Stage III–IV human ovarian carcinoma specimens were procured through Research Pathology Services at Dartmouth-Hitchcock Medical Center under institutional approval (CPHS17702). Single cell suspensions or cDNA were generated as we previously described (Huarte et al., 2008b). The primary mouse cell line, UPK10, was generated by culturing a mechanically dissociated B6 LSL-K-rasG12D/+p53loxP/loxP primary ovarian tumor mass. Tumor cells were passaged a total of 10× and lead to terminal tumor masses 28 d after i.p. injection in WT animals. HOSEpiC are epithelial cells from healthy ovaries cryopreserved either at primary or passage one cultures (ScienCell Research). Generation of transgenic mice. To generate the LSL-K-rasG12D/+p53loxP/loxP model, we used Krastm4Tyj and Trp53tm1Brn mice (Jackson et al., 2001; Jonkers et al., 2001), obtained from the NCI Mouse Models of Human Cancers Consortium. For the indicated experiments, mice were brought to a C57BL/6 background. Chimera generation and antibody-mediated depletion. BM cells (10 × 106) isolated from ITGAX DTR-GFP mice were injected intravenously into B6 K-rasG12D/+p53loxP/loxP after undergoing lethal doses of gamma irradiation, as we previously described (Huarte et al., 2008a). Mice were checked for complete reconstitution by identifying GFP+CD45+CD11c+ cells in peripheral blood ∼6 wk after reconstitution. ITGAX-DTR (DT) or WT mice (n = 3/group) were inoculated intrabursally with 2.5 × 107 plaque-forming units of adenovirus expressing Red-Cherry (Gene Transfer Vector Core, University of Iowa). CD8 T cells were depleted through intraperitoneal administration of rat anti–mouse CD8 antibodies (clone# YTS 169.4; BioXCell), 2 d before intrabursal administration of 500 µg adenovirus-Cre. Injections were repeated at days 5, 12, and 19 (250 µg each). Control mice received a rat IgG2b isotype control (clone# LTF-2; BioXCell). Proliferation and suppression assays. For T cell proliferation assays, day 7 BMDCs, generated as previously described (Scarlett et al., 2009), were cultured overnight with either freeze-thawed lysed UPK10 or dissociated primary tumors. BMDCs were added to cultures of CFSE (Invitrogen)-labeled T cells at a 10:1 ratio and were analyzed 3 d later by flow cytometry. For DC proliferation assays (Fig. 6 A, schematic), Pan T cell isolation (Miltenyi Biotec) was performed using spleens taken from p53/K-ras animals with advanced (>50 d after AdvCre injection) tumors. CD3+ T cells were then cultured for ∼4–5 d with tumor-pulsed BMDCs as described at a 10:1 ratio to generate tumor-specific T cells. Tumor-specific T cells were then CFSE labeled and added to V-bottom 96-well plates which contained sorted DCs (10:1 ratio). Cultures were analyzed by flow cytometry 5 d later. For the suppression assay (Fig. 6 C, schematic), tumor-pulsed BMDCs were added to CFSE-labeled tumor-specific T cells (1:1), followed by addition of unpulsed BMDCs or sorted DCs (1:1:1 ratio) in V-bottom 96-well plates. Analyses were performed 3 d later. Neutralization assays. PGE2 (Cayman Chemical) and mature TGF-β1 and IL-6 (both eBioscience) were quantified by ELISA in media conditioned for 2 d by >90% confluent UPK10 tumor cells. When indicated, neutralizing antibodies against mouse PGE2 (7 µg/ml; 2B5; Cayman Chemical), TGF-β1 (5 µg/ml; 2Ar2; Abcam) or IL-6 (4 µg/ml; PeproTech) were added to the media. Goat anti–mouse IgG (Jackson ImmunoResearch Laboratories, Inc.) was used as a control. Histological analysis. For frozen tissue, organs of mice were collected and embedded in Tissue-Tek OCT. For paraffin-embedded tissue, organs were fixed in 4% formaldehyde overnight at 4°C. Fixed sections (8 µm) were then made from frozen or paraffin-embedded tissue blocks. For analysis of tumor histological type, pan Cytokeratin (AE1+AE3) 1:50 dilution, smooth muscle actin (SMA; EP184E) 1:500 dilution, Desmin (Y266) 1:500 dilution, Cytokeratin 8 (ab59400) 1:250 dilution, and Vimentin (RB202) 1:200 dilution were purchased from Abcam and immunohistochemical analyses were performed by the Dartmouth Pathology Translational Research Core (Lebanon, NH). For immunohistochemistry of leukocytes, tissues were blocked using α-CD32, followed by staining with anti–mouse biotinylated CD45 (104), MHCII (M5/114.15.2), or APC-conjugated Dec205 (NLDC-145; all obtained from BioLegend). Completion of immunohistochemical procedure was performed according to the manufacturer’s instructions (Vector Laboratories). Slides were then viewed at various magnifications using a fluorescence microscope (Nikon) and the NIS-Element Imaging software. Flow cytometry. Flow cytometry was performed on a FACSCanto (BD). Sorting was performed on a FACSAria sorter (BD). Anti–mouse antibodies: CD45 (30-F11), CD11b (M1/70), DEC205 (NLDC-145), F4/80 (BM8), GR1 (RB6-8C5), CD3 (145-2C11), CD8β (YTS156.7.7), CD8α (53–6.7), CD4 (GK1.5), CD25 (PC61.5), and PDL-1 (10F.9G2; all obtained from BioLegend); and Foxp3 (FJK-16s), CD11c (N418), and MHC-II (NIMR-4; eBioscience). Anti–human antibodies: CD45 (H130), DEC205 (HD30), CD11c (3.9), CD3 (OKT3), CD11b (ICRF44), HLA-DR (L243), CD14 (HCD14), and CD20 (2H7; all obtained from BioLegend). The purity of FACS-sorted populations was >90%. Quantitative real-time PCR and sequencing. Messenger RNA copy number of various loci was assessed by quantitative real-time PCR using an Real Time PCR Machine (Applied Biosystems). Primers for RT-PCR experiments to detect human K-ras expression using SYBR green: Kras forward, 5′-TGTGGACGAATATGATCCAACAA-3′; and Kras reverse, 5′-TCCTCATGTACTGGTCCCTCATT-3′. Primers for mouse and human GAPDH expression using TaqMan: GAPDH forward, 5′-CCTGCACCACCAACTGCTTA-3′; GAPDH reverse, 5′-AGTGATGGCATGGACTGTGCT-3′; and probe (FAM/TAMRA), 5′-CCTGGCCAAGGTCATCCATGACAACTTT-3′. Cytokine/chemokine detection. Sorted tumor-associated cells from either p53/K-ras mice or Stage III–IV human ovarian carcinoma specimens were stimulated for 4 h with 50 ng PMA/1 µg/ml ionomycin in complete RPMI containing 10% FBS. Supernatants were used for cytokines and chemokines using a human or mouse Custom-Plex panel cytokine assay (Bio-Rad Laboratories), according to the manufacturer’s instructions. Arginase activity assay. Cells from either p53/K-ras mice (tumor and spleens) or Stage III–IV human ovarian carcinoma specimens were sorted. Quantitative colorimetric arginase determination was performed using an Arginase Activity detection kit (BioAssay Systems). In brief, 0.05–0.25 × 106 cells were washed and lysed for 10 min in 50 µl of 10 mM Tris-HCl, pH 7.4, containing 0.15 mM pepstatin A, 0.2 mM leupeptin, and 0.4% (vol/vol) Triton X-100. Supernatants from lysates were then used to complete the assay according to the manufacturer’s instructions. For human analysis, we cultured cells with agonistic CP-870,893 αCD40 monoclonal antibody and poly (I:C) as previously described (Scarlett et al., 2009). We obtained the CP-870,893 monoclonal antibody from Pfizer. ELISPOT. Total or sorted cells were obtained from dissociated spleens, renal DLNs, or tumors of p53/K-ras or WT controls. T cells were then co-cultured for 72 h in coated and blocked ELISPOT plates, in a 10:1 ratio among day 7 BMDC or sorted DC, which were previously pulsed (4 h) with freeze-thawed lysed UPK10 cells or resected primary tumor (10 DC/1 tumor cell). All cultures were maintained in complete RPMI containing 10% FBS. Analysis was then continued according to manufacturer’s protocol (IFN-γ, eBioscience; and Granzyme B, R&D Systems). Immunoblotting. TRIzol reagent (Invitrogen) was used to obtain protein from either stage III or IV human solid tumor specimens. In brief, frozen tissues were cut into tiny pieces, and then added to complete RPMI where they were further macerated using the end of a syringe’s plunger. The dissociated tissues were spun and the pellets were added to TRIzol, and the completion of the protein extraction was performed according to the manufacturer’s instruction. Proteins were diluted in 7 µl Laemmli buffer, boiled, loaded onto a 12% Ready Gel Tris-HCL gel (Bio-Rad Laboratories), transferred to a nitrocellulose membrane, blocked, and incubated with the indicated primary Ab. Immunoreactive bands were developed using horseradish peroxidase–conjugated secondary Abs (Bio-Rad Laboratories) and chemiluminescent substrate (GE Healthcare). Human KRAS, β-actin, and β-tubulin were detected using a mouse anti–human mAb (clone no. ab55391), rabbit anti–human (ab8227), and goat anti–human (ab21057) antibodies, respectively (all from Abcam). Statistical analyses. Differences between the means of experimental groups were analyzed using the Mann-Whitney or the χ2 test. Survival was analyzed with the Log-rank test, both using Prism 4.0 software (GraphPad Software). Proliferation indices, defined as the mean number of cell divisions that the responding cells underwent, were calculated using FlowJo software (Tree Star).
Author and article information
Journal
Journal ID (nlm-journal-id): 9604648
Journal ID (pubmed-jr-id): 20305
Journal ID (nlm-ta): Nat Biotechnol
Journal ID (iso-abbrev): Nat. Biotechnol.
Title:
Nature biotechnology
ISSN
(Print):
1087-0156
ISSN
(Electronic):
1546-1696
Publication date Nihms-submitted: 26
November
2014
Publication date
(Electronic):
15
December
2014
Publication date
(Print):
January
2015
Publication date PMC-release: 01
July
2015
Volume: 33
Issue: 1
Pages: 97-101
Affiliations
[2
]Technology Access Foundation (TAF) Academy, Fred Hutchinson Cancer Research Center,
Seattle, Washington, USA
[3
]Department of Microbiology and Immunology, The Geisel School of Medicine at Dartmouth,
Lebanon, New Hampshire, USA
Author notes
Article
Manuscript ID: NIHMS644838DOI: 10.1038/nbt.3104
PMC ID: 4289408
PubMed ID: 25503382
SO-VID: 3c511747-2b9a-4256-98e6-b78c29850b2d
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ScienceOpen disciplines: Biotechnology
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ScienceOpen disciplines: Biotechnology