Innate immune activation by checkpoint inhibition in human patient-derived lung cancer tissues

  1. Teresa WM Fan  Is a corresponding author
  2. Richard M Higashi
  3. Huan Song
  4. Saeed Daneshmandi
  5. Angela L Mahan
  6. Matthew S Purdom
  7. Therese J Bocklage
  8. Thomas A Pittman
  9. Daheng He
  10. Chi Wang
  11. Andrew N Lane  Is a corresponding author
  1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, United States
  2. Department of Toxicology and Cancer Biology, University of Kentucky, United States
  3. Markey Cancer Center, University of Kentucky, United States
  4. Departement of Surgery, University of Kentucky, United States
  5. Departement of Pathology and Laboratory Medicine, University of Kentucky, United States
  6. Department of Neurosurgery, University of Kentucky, United States
  7. Department Internal Medicine, University of Kentucky, United States

Abstract

Although Pembrolizumab-based immunotherapy has significantly improved lung cancer patient survival, many patients show variable efficacy and resistance development. A better understanding of the drug’s action is needed to improve patient outcomes. Functional heterogeneity of the tumor microenvironment (TME) is crucial to modulating drug resistance; understanding of individual patients’ TME that impacts drug response is hampered by lack of appropriate models. Lung organotypic tissue slice cultures (OTC) with patients’ native TME procured from primary and brain-metastasized (BM) non-small cell lung cancer (NSCLC) patients were treated with Pembrolizumab and/or beta-glucan (WGP, an innate immune activator). Metabolic tracing with 13C6-Glc/13C5,15N2-Gln, multiplex immunofluorescence, and digital spatial profiling (DSP) were employed to interrogate metabolic and functional responses to Pembrolizumab and/or WGP. Primary and BM PD-1+ lung cancer OTC responded to Pembrolizumab and Pembrolizumab + WGP treatments, respectively. Pembrolizumab activated innate immune metabolism and functions in primary OTC, which were accompanied by tissue damage. DSP analysis indicated an overall decrease in immunosuppressive macrophages and T cells but revealed microheterogeneity in immune responses and tissue damage. Two TMEs with altered cancer cell properties showed resistance. Pembrolizumab or WGP alone had negligible effects on BM-lung cancer OTC but Pembrolizumab + WGP blocked central metabolism with increased pro-inflammatory effector release and tissue damage. In-depth metabolic analysis and multiplex TME imaging of lung cancer OTC demonstrated overall innate immune activation by Pembrolizumab but heterogeneous responses in the native TME of a patient with primary NSCLC. Metabolic and functional analysis also revealed synergistic action of Pembrolizumab and WGP in OTC of metastatic NSCLC.

Introduction

New checkpoint inhibitor-based immunotherapy, such as the use of programmed death one receptor (PD-1, CD279, or PDCD1) antibody Pembrolizumab (Pembro) has significantly improved the survival of lung and other cancer patients. However, even after stratification, a large fraction of the patient population shows variable efficacy, resistance development, and intolerable toxicity (Arlauckas et al., 2017; Ayers et al., 2017; Garon et al., 2015; Meyers et al., 2018; Morgensztern and Herbst, 2016; Rizvi et al., 2015). Clearly better therapeutic prediction and understanding of the drug action are needed to improve patient care and outcomes. Functional heterogeneity of the tumor microenvironment (TME) is crucial to modulating drug responses including resistance development (Dalton, 1999; Ostman, 2012; Qu et al., 2019; Trédan et al., 2007; Wang et al., 2009). However, our understanding of individual patients’ TME that impacts drug response is hampered by lack of appropriate models.

Multiple preclinical models have been established for basic and translational cancer research, ranging from 2D/3D cell cultures, 3D patient-derived organoids (PDO), ex vivo organotypic tissue slice cultures (OTC), to in vivo xenograft and patient-derived xenograft (PDX) animal models (Fan et al., 2020). Each model has its advantages and limitations. The use of tissue slices (with no culturing involved) was pioneered by O. Warburg who demonstrated enhanced lactic fermentation under aerobic conditions (Warburg, 1924), which is now recognized as a hallmark of cancers (Hanahan and Weinberg, 2011). We have adopted and improved this approach by culturing ex vivo lung tissue slices derived from NSCLC patients. We have shown that such ex vivo cultures recapitulate the metabolic reprogramming observed in vivo (Sellers et al., 2015). This model is versatile in experimental manipulations and retains patients’ native 3D architecture/tissue microenvironment including infiltrated immune cells, which is not represented by multiply passaged mouse PDX or 3D cell models including PDO. Furthermore, the matched cancer (CA) and non-cancer (NC) tissue design provides the capacity for distinguishing on- and off-target drug actions in individual patients (Lane et al., 2016). We previously showed that two different PD lung OTC treated with the natural product immune activator (β-glucan) displayed distinct responses of macrophage (MΦ) metabolism that was related to variable tissue damage (Fan et al., 2016).

It is now widely recognized that metabolic reprogramming is crucial to immune cell activation to meet the demand for energy/anabolic metabolism (Gnanaprakasam et al., 2017; O’Neill and Pearce, 2016; Palmieri et al., 2020) and specific immunomodulatory events. Notably, succinate production in MΦ stabilized HIF1α, resulting in the release of proinflammatory IL-1β (Tannahill et al., 2013). Tryptophan (Trp) catabolism to quinolinate fuels de novo NAD+ synthesis and blockade of this pathway led to a pro-inflammatory state in aged MΦ (Minhas et al., 2019). Reprogrammed metabolic events in the TME can also favor tumor immune escape (Chang et al., 2015; Fischer et al., 2007; Ganeshan and Chawla, 2014; Ghesquière et al., 2014; Lim et al., 2017; Sica and Bronte, 2007). For example, enhanced glycolysis (e.g. induced by the expression of checkpoint ligand PD-L1) in cancer cells can deplete glucose leading to T-cell ‘anergy’ in the TME (Chang et al., 2015; Fischer et al., 2007; Lim et al., 2017). The resulting excess release of lactate and protons into the TME can also deactivate infiltrating MΦ and T cells (Fischer et al., 2007; Gurusamy et al., 2017; Munn and Bronte, 2016; Parker et al., 2015). The expression of PD-L1 receptor, PD-1 was also shown to negatively regulate T-cell function and survival by modulating T-cell metabolism (Bettonville et al., 2018; Patsoukis et al., 2015; Tkachev et al., 2015). Moreover, tumor indoleamine 2,3-dioxygenase (IDO) depleted Trp in the TME by oxidation to kynurenine, which led to T-cell inhibition (Joyce and Fearon, 2015; Munn and Mellor, 2013). All of these immunomodulatory events were primarily learned from animal models or in vitro human cell cultures. The question is whether they are recapitulated in the patient tumor tissues.

To address this question, we prepared ex vivo lung OTC procured from one each patient with primary and brain-metastasized (BM) NSCLC. They were subject to Pembro ± β-glucan treatments with 13C6-glucose or 13C5,15N2-Gln as tracers for tracking perturbations in metabolic networks by stable isotope-resolved metabolomic (SIRM) analysis (Fan et al., 2016; Fan et al., 2019; Sellers et al., 2015). We found that Pembro activated the pentose phosphate pathway (PPP), glycogen turnover, and Gln uptake but blocked Gln use in the Krebs cycle, itaconate synthesis, and quinolinate buildup in the primary NSCLC OTC. For the metastatic NSCLC OTC, Pembro or β-glucan alone had little metabolic effect but the combination treatment suppressed the PPP, glycogen synthesis, and nucleotide synthesis. Reduced mitotic index and significant tissue damage were evident in Pembro-treated primary and Pembro + β-glucan-treated BM-NSCLC OTC. Metabolic changes in the primary OTCs were correlated with the release of pro-inflammatory effectors and reduced expression of cancer cell/anti-inflammatory MΦ markers. These changes point to innate immune activation by Pembro, but different TMEs responded variably as shown by digital spatial profiling (DSP) and/or immunofluorescence analysis.

Results and discussion

PD-1+, CD8+ T cells are present in both primary and metastatic NSCLC tumors

We performed multiplex immunofluorescent (mIF) staining on the primary (from patient UK131) and metastatic (from patient UK2035) NSCLC tissues to characterize the tumor cell type, the status of the checkpoint proteins, and immune cell properties (see Supplementary file 1, for staging and demographics). As shown in Supplementary file 2, the UK131 tumor stained positive for KRT5 (A), which is consistent with its clinical classification as lung squamous cell carcinoma (LSCC) (Xiao et al., 2017). Although the KRT5+ cancer cells were positive for HIF1α, this staining occurred much more in the cytoplasm than in the nucleus, which suggests that the major fraction of HIF1α was not transcriptionally active. Also present were the smaller non-cancer HIF1α+ cells, which are likely infiltrated immune cells (A). More notable was the presence of PD-1 expressing CD8+ T cells (B), which indicates a high likelihood of this tissue to respond to Pembro treatment. Also evident were PD-L1 expressing CD206+ cells (C), which are characteristic of tumor-associated MΦ (TAM) (Van Overmeire et al., 2014) and reflective of an immunosuppressive TME (Lu et al., 2019).

Positive mIF staining of chromogranin A (CgA) and NCAM1 (Supplementary file 3) confirmed the clinical classification of the metastatic UK2035 tumor as a lung neuroendocrine tumor (Kashiwagi et al., 2012). PD-1 expressing CD8+ T cells and PD-L1+ cells were also present in this tumor, with the former cells far fewer in numbers than the latter cells (Supplementary file 3).

Primary tumor tissue slice cultures of UK131 patient respond to Pembro treatment

Pembro activates the pentose phosphate pathway and glycogen synthesis in UK131 CA OTC

We treated the matched pair of CA and NC lung OTC of patient UK131 with Pembro for 24 hr in the presence of 13C6-glucose (13C6-Glc) or 13C5,15N2-Gln. We found that the CA OTC responded to Pembro with a metabolic shift. Namely, Pembro enhanced 13C6-Glc-derived 13C labeling in most of the PPP metabolites compared to the control (Ctl) (Figure 1). These included 13C6-glucose-6-phosphate (G6P) (6, a), 13C5-ribulose/ribose-5-phosphate (R5P) (5, c), 13C7-sedoheptulose-7-phosphate (S7P) (7, d), 13C4-erythrose-4-phosphate (E4P) (4, e), and 13C6-fructose-6-phosphate (F6P) (6, f), but not 13C6-6-phosphogluconate (6 PG) (6, b). These data point to activation of the PPP with a partial block at the glucose-6-phosphate dehydrogenase (G6PD) step in the oxidative branch. As such, the non-oxidative (Nonox) branch reactions catalyzed by the transketolase (TK) and transaldolase (TA) contributed more to the 13C incorporation into R5P, S7P, E4P, and F6P in Pembro versus Ctl-treated CA OTC. This agrees with the Pembro-induced higher buildup of scrambled 13C isotopologues (Scr) of these metabolites (Figure 1). In addition to PPP activation, Pembro enhanced 13C labeling of glycogen (i) and its direct precursor UDP-glucose (UDPG, h) (Figure 1), which suggests stimulation of glycogen synthesis by Pembro. None of these changes were evident in the matched NC OTC, which indicates that these effects are tumor specific.

Figure 1 with 1 supplement see all
Pembro activates the PPP and glycogen synthesis in primary CA, but not in NC lung tissues.

Matched pairs of fresh cancerous (CA) and non-cancerous (NC) lung tissue slice cultures of UK131 patient were treated with 40 μg/mL Pembro + 13C6-glucose (13C6-Glc) for 24 hr before extraction for polar metabolites, as described in the Experimental. The diagram traces the atom-resolved 13C6-Glc transformation through the PPP and glycogen synthesis, the green arrows and dashed curves delineate the reactions of transketolase (TK) and transaldolose (TA) that scramble 13C labeling in PPP metabolites, and denotes the block at glucose-6-phosphate dehydrogenase (G6PD) step. Panels (a–h) were obtained from IC-UHR-FTMS analysis and panel (i) from 1D HSQC NMR analysis (n = 3). X-axis denotes the number of 13C or scrambled 13C (Scr) in each metabolite. : 13C; : 12C; : NC control (Ctl); : NC Pembro; : CA Ctl; : CA Pembro; G6P: glucose-6-phosphate; 6 PG: 6-phosphogluconate; R5P: ribulose/ribose-5-phosphates; S7P: sedoheptulose-7-phosphate; Gly3P: glyceraldehye-3-phosphate; E4P: erythrose-4-phosphate; F6P: fructose-6-phosphate; G1P: glucose-1-phosphate; UDPG: UDP-glucose; Glyco: glycogen; UDPGlcNAc: UDP-N-acetylglucosamine. Panels (ah) show mean ± sem (n = 3). *p<0.05.

Pembro blocks Gln oxidation through the Krebs cycle in UK131 CA lung tissues

With 13C5,15N2-Gln as tracer, Pembro moderately increased 13C-Gln uptake in CA OTC, compared with the Ctl. This was accompanied by enhanced release of 13C-Glu and 13C-succinate into the medium (Figure 2A–a), which could at least in part account for Pembro-induced increase in Gln uptake. In comparison, there were only minor changes in the glucose uptake and lactate released into the medium (Figure 2A–b). Thus, Pembro enhanced glutaminolysis (Gln conversion to Glu and succinate) but had a negligible effect on glycolysis. We also tracked intracellular oxidation of glutaminolytic products via the Krebs cycle (Figure 2B). In CA OTC, Pembro modestly depleted the first two products 13C5-Glu (C5Nx, a)/ 13C5-αKG (C5, b) and subsequent Krebs cycle products (13C4- and other 13C-isotopologues of succinate, fumarate, malate, citrate, and Asp) (c–g). 13C labeling of glutathione (GSH, h) was less affected by the Pembro treatment. Blockade of the glutaminase (GLS) activity and/or diversion of Glu to GSH metabolism could account for these Pembro-induced changes in the 13C labeling patterns of tissue metabolites. In addition, GSH synthesis requires cystine uptake from the medium in exchange for Glu release via the xCT antiporter (Shih et al., 2006), which would contribute to the buildup of 13C-Glu in the medium and add to the depletion of tissue 13C-Glu. Likewise, increased release of 13C-succinate to the medium would further deplete tissue succinate. Again, none of these changes were evident in the matched NC counterparts.

Figure 2 with 1 supplement see all
Pembro enhances Gln uptake/release of glutaminolytic products while blocking Gln-fueled Krebs cycle activity in primary CA but not in NC lung tissues.

A separate set of CA and NC lung tissue slices from UK131 was treated with 40 μg/mL Pembro plus 13C5,15N2-Gln (13C515N2Q) for 24 hr before extraction and analysis for polar metabolites as in Figure 1. Diagram in (A) depicts the glutaminolytic production of 13C-Glu and release into the medium, 13C5-Glu transformation through the Krebs cycle, and 13C3-pyruvate/lactate production from the malic enzyme (ME) pathway, 13C-itaconate production from c-aconitate (c-Acon), Trp uptake and oxidation to quinolinate (QA) as well as unlabeled lactate production from glucose (Glc) via glycolysis. The diagram in (B) traces atom-resolved transformation of 13C5,15N2-Gln through the Krebs cycle (both PDH- and PCB -initiated) and ME () pathway. Panels (a, b) in (A) were obtained from 1D HSQC analysis while the rest of all panels from IC-UHR-FTMS analysis. Numbers in X-axis denote those of 13C while x indicates 1–2 15N and N is 15N1. : 12C; : 14N; : 15N; F1,6BP: fructose-1,6-bisphosphate; Lac: lactate; AcCoA: acetyl coenzyme A; αKG: α-ketoglutarate; Suc: succinate; Suc CoA: succinyl CoA; GSH: glutathione; GLS: glutaminase; PDH: pyruvate dehydrogenase; PCB: pyruvate carboxylase. Panels a-h display mean ± sem, with n = 3.

Pembro-induced metabolic shift in CA OTC is akin to that induced by M1-type polarization of human MΦ

Activation of the PPP and glycogen metabolism is known to occur during proinflammatory or M1-type polarization of murine MΦ and model human MΦ THP-1 cells by lipopolysaccharides (LPS) (Ma et al., 2020; Nagy and Haschemi, 2015). PPP and glycogen metabolism via PPP (Ma et al., 2020) help sustain NADPH production in activated MΦ to maintain redox homeostasis and fuel the demand for lipid and deoxynucleotide synthesis. To verify such metabolic shift in activated human MΦ, we isolated peripheral blood monocytes (PBMC) from a male volunteer age-matched to the cancer patient, followed by differentiation into the M0 state and treatment with LPS+ interferon-gamma (IFNγ) for classical M1 polarization or with IL-4+ IL-13 for alternative M2 polarization in the presence of 13C6-Glc. Comparing M1- versus M2-type MΦ, we saw very similar shift in the 13C labeling patterns of the PPP and glycogen products (Figure 1—figure supplement 1) as those observed for the Pembro- versus Ctl-treated CA OTC (Figure 1A). Particularly noted were the break in G6P (a) to 6 PG (b) conversion and the enhanced buildup of 13C scrambled products of PPP (c–f) in M1- versus M2-type MΦ (Figure 1—figure supplement 1); the latter is consistent with Nonox PPP being the major source of pentose phosphates in LPS-activated murine MΦ (Haschemi et al., 2012).

We also observed similarities in the shift of 13C5,15N2-Gln metabolism induced by M1 polarization of human MΦ (Figure 2—figure supplement 1) compared with those elicited by Pembro in CA OTC (Figure 2). These included enhanced release of 13C-succinate (A–a), lack of effect on the excretion of glycolytic lactate, reduced Gln oxidation through glutaminolysis and the Krebs cycle (B–a to g), and lack of attenuation in GSH synthesis (B–h) (Figure 2—figure supplement 1). However, some differential responses were also evident, notably reduced 13C-Glu release (A–a) as well as increased buildup of 13C-itaconate (A–c) and unlabeled quinolinate (QA, A–d) in M1 versus M2-type Mϕ (Figure 2—figure supplement 1) as opposed to the lack of or opposite effect observed for Pembro- versus Ctl-treated CA OTC (Figure 2A). These differences could reflect the influence of TME on MΦ metabolism and/or metabolic contribution of other cell types in the TME.

Besides metabolic shifts, we examined the culture media for secreted immune effectors. The release of proinflammatory cytokines/chemokines into the media was consistently enhanced by the Pembro treatment of CA OTC, as shown in Figure 3A–F. Some of these Pembro-induced changes were evident in NSCLC tumor PDOs (Mediavilla-Varela et al., 2017). Although the none of the Pembro effects on CA OTC reached statistical significance of p-value ≤ 0.05, consistent trend was seen that points to M1 type polarization. This is corroborated by the increased release of the same set of effectors (IL-6, TNFα [Mercalli et al., 2013], IL-8 [Meniailo et al., 2018], MIP-1α or CCL3 [Maurer and von Stebut, 2004], IFNγ, and IFNα2 [Kumagai et al., 2007]) in M1- versus M2-polarized human MΦ (Figure 3—figure supplement 1). It is interesting to note that MIP-1 are chemokines that regulate both pro-inflammatory responses and homeostasis (wound healing; Maurer and von Stebut, 2004).

Figure 3 with 1 supplement see all
Pembro enhances the release of proinflammatory factors in slice cultures of NSCLC OTC from UK131 patient.

Media (A–F) from the CA samples in Figure 2 were analyzed for cytokines and chemokines as described in the Experimental. C-Glc or C-Gln (): Ctl + 13C6-Glc or 13C5,15N2-Gln; P-Glc or P-Gln (): 40 μg/mL Pembro + 13C6-Glc or 13C5,15N2-Gln. Mean ± se (n = 3) are displayed.

Pembro-induced metabolic shifts are accompanied by CA tissue damage and TME-dependent immune responses

Histological analysis of Pembro-treated CA OTC sections by hematoxylin and eosin (H&E) staining revealed extensive tissue damages including the presence of MΦ in apoptotic regions, as shown in Figure 4A. mIF staining of separate sections of the same OTC confirmed increased apoptosis (red caspase fluorescence), which was accompanied by decreased mitotic index (green PCNA fluorescence) (Figure 4B). Also evident were the decrease in KRT5+/PD-L1+, KRT5+ cancer cell as well as PD-1+ and PD-1+/CD8+ T-cell populations. The CA OTC response is expected based on Pembro-mediated activation of exhausted PD-1+ T cells that possess anti-tumor activity (Graves et al., 2019).

Heterogeneous cellular and functional responses of UK131 tumor tissue slice culture to Pembro.

The treated tissue slices from Figure 2 were subsampled, paraffin-embedded, and sectioned into 4 µm slices for H&E staining (A), mIF staining (B), and digital spatial profiling, as described in the Experimental. White circles in (C) are regions of interest (ROIs) in 100 and 200 µm in diameter, which were selected based on the abundance of cancer cells (panCytokeratin or panCK in green; e.g. 4, 6 in Control; 3, 7–8, 9, 12 in Pembro), CD8+ T cells (CD8 in red; e.g. 3 in Control; 1, 10 in Pembro), MΦ (CD68 in yellow; e.g. 2 in Control; four in Pembro), and mixed cell populations (e.g. 1, 5 in Control; 2, 5, 11 in Pembro). ROIs 7–12 for control tissue was not shown. These ROIs were probed for the expression level of 58 different markers of cancer and immune cell functional states using Oligo-barcoded antibodies, as described in the Experimental. Eleven markers that were differentially expressed region-wide between Control and Pembro treatments are shown as heat map in (D). The scale in D is log2. ROI-specific quantification of notable markers for Ctl- and Pembro-treated tissues is shown in (E), where the ratios displayed were marker-specific Oligo counts were normalized to the geometric mean of the house keeping genes GAPDH, S6, and Histone H3. Some ratios were multiplied or divided for display purposes.

To determine how the response of CA tissue varied with the TME, we analyzed another section of the same OTCs by DSP. DSP is a new technology that enables high-plex analysis of many protein markers in different regions of interest (ROIs) of FFPE tissue sections (Zugazagoitia et al., 2020). We chose 12 ROIs at 100 or 200 µm diameter for each Ctl and Pembro-treated tissue sections (Figure 4C and data not shown) as described previously (Fan et al., 2020). The choices were based on TME regions enriched in T cells (CD8+, red), MΦ (CD68+, yellow), cancer cells (panCK+, green), and combinations of the three cell types. ROI-dependent analysis of 58 protein markers revealed 11 markers whose expression was attenuated across most ROIs of Pembro-treated versus Ctl tissue sections (Figure 4D). These included Ki67 as well as cancer cell markers EpCAM and Her2. The decreased expression is consistent with tissue damage and reduced cancer cell population as observed by H&E and mIF imaging (Figure 4A,B). Also evident was the reduced expression of M2-type MΦ markers, CD163 and Arg1 as well as regulatory T (Treg) cell marker CD25. CD25+ Treg cells are known to induce M2-type polarization in co-cultured monocytes, as evidenced by the upregulation of CD163 and downregulation of HLA-DR expression (Tiemessen et al., 2007). Thus, CD25 suppression may be related to reduced CD163 expression in the Pembro-treated TME. Although there was no consistent increase in HLA-DR expression across ROIs, we saw an overall increase in the ratio of this M1–MΦ marker to the M2–MΦ marker Arg1 in Pembro- versus Ctl-treated CA tissues (Figure 4E). This was also the case for another human M1–MΦ marker IDO1. These data point to repolarization of M2- to M1-type MΦ in the Pembro-treated CA tissue. In contrast to the response of the Treg marker CD25, the ratio of a T-cell activation marker iCOS (Dong et al., 2001) to T-cell marker CD4 largely increased (Figure 4E) along with decreased expression of the checkpoint protein CTLA4 (Figure 4D) across all ROIs in response to Pembro. Reduced Treg cell occurrence was commonly noted in CA tissues from Pembro-treated cancer patients (Kumar et al., 2020). However, enhanced iCOS expression in T cells was not known to be induced by anti-PD-1 treatment (Wei et al., 2017). It should be noted that since no immune cell infiltration can occur in our OTC model, the Pembro-induced T-cell or MΦ marker response was derived from the resident populations only.

Moreover, we observed variable ROI-dependent distributions of immune functional and cancer cell markers in relation to the mitotic index marker Ki-67 in Pembro- and Ctl-treated CA tissues (Figure 4E). In particular for Pembro-treated tissues, panCK+ cancer cells-rich ROIs 3, 7, 9, and 12 had much higher granzyme B (GZMB) relative to Ki-67 levels than ROIs 6 and 8. ROIs 3, 7, 9, and 12 also displayed higher iCOS/CD4 ratios than ROIs 6 and 8. These data point to more activated T cells and reduced proliferative capacity of cancer cells in the former than the latter regions. We also noted attenuated expression of a lung cancer driver protein Her2 (Garrido-Castro and Felip, 2013) in ROIs 6/8 versus ROIs 3/7/9/12, which suggests a difference in cancer cell property between the two sets of ROIs. Moreover, ROI six displayed very high IDO1/Arg1 and HLA-DR/Arg1 ratios than other ROIs including ROI 8. This could reflect a particularly high polarization of TAM toward M1-type and/or IDO1 overexpression in cancer cells in ROI 6. In either case, cancer cells in this TME appeared to remain proliferative. As for ROI 8, the proliferative capacity was the highest with the lowest IDO1 or HLA-DR to Arg1 ratios but a comparable level of cytotoxic GZMB and ICOS/CD4 ratio to those of ROIs 3/7/9/12. These data implicate the presence of Pembro-resistant cancer cells in ROIs 6 and 8, which can overcome M1 polarization in ROI six and T-cell activation in ROI 8. If ROI six cancer cells were high in IDO1 activity, this can deplete Trp in the TME while producing immunosuppressive metabolites such as kynurenine, both of which could contribute to immune evasion (Li et al., 2019; Moffett and Namboodiri, 2003) and thus cancer cell resistance. IDO1 overexpression occurred in NSCLC and higher serum kynurenine to Trp ratios/QA levels were associated with NSCLC disease progression (Bianco et al., 2019; Botticelli et al., 2018).

Our metabolomic data showed that QA built up in response to M1-MΦ activation (Figure 2—figure supplement 1), which presumably resulted from a block in the downstream enzyme quinolinate phosphoribosyl-transferase, which is involved in de novo NAD+ synthesis (Minhas et al., 2019). Trp-fueled NAD+ repletion is required for mitochondrial signaling and metabolism during immune resolution. QA depletion by Pembro in CA OTC (Figure 2A–d) is unlikely to result from M2-type MΦ polarization as Arg1 expression was down (Figure 4D). Instead, it is consistent with excess Trp consumption by IDO1-overexpressing cancer cells, thereby interfering with immune activation.

Metastatic tumor OTCs of UK2035 patient respond to Pembro+WGP co-treatment

Pembro+WGP blocks the Krebs cycle, pentose phosphate pathway, and glycogen/nucleotide synthesis in UK2035 CA lung OTCs

We treated ex vivo OTCs of BM NSCLC tissues of patient UK2035 with none (Ctl), Pembro, WGP, or Pembro + WGP (P + W) in the presence of 13C6-Glc for 24 hr. We found no consistent changes in the 13C labeling of the glycolytic and Krebs cycle intermediates or end products in response to Pembro () or WGP () treatment (a, c–h), except for the WGP-elicited reduction of F1,6BP (b) (Figure 5A). Nor were there consistent changes for GSH and itaconate derived from the Krebs cycle (k–l). However, P + W treatment () blocked 13C incorporation into tissue F1,6BP, pyruvate (c), citrate (e), c-aconitate (f), Asp (i), and Glu (j) but not the uptake of 13C-Glc nor the release of 13C-Lac into media. These data suggest that P + W disrupted the first half of the Krebs cycle activity (PDH to IDH), but not glycolysis as a whole.

Pembro + WGP attenuates central energy and anabolic metabolism in OTCs of brain-metastasized NSCLC tissues from UK2035 patient.

CA lung OTCs of UK2035 patient were treated with Ctl (), 40 μg/mL Pembro (), 0.1 mg/mL WGP () (n = 3), or Pembro + WGP combined (P + W ) (n = 2) in the presence of 13C6-Glc for 24 hr before extraction for polar metabolites and analysis by IC-UHR-FTMS, as described in the Materials and Methods. The diagrams in (A–D) depict atom-resolved transformation of 13C6-Glc via glycolysis+ the Krebs cycle, the PPP+ glycogen synthesis pathway, and pathways of pyrimidine and purine nucleotide synthesis, respectively. Base in X-axis of (C) and (D) denotes 13C-labeled isotopologues of pyrimidine and purine bases. OMP: orotidine 5’-monophosphate; IMP: inosine 5’-monophosphate. All other symbols and abbreviations are as in Figures 12. Data are displayed as mean ± sem. *p<0.05.

We then tracked 13C incorporation into the products of the PPP and glycogen metabolism in treated UK2035 CA OTCs as shown in Figure 5B. The combined P + W treatment substantially depleted all major 13C labeled products of PPP (a–f). Neither Pembro nor WGP treatment alone affected the 13C labeling of the PPP products except for the depletion of 13C scrambled 6 PG (Scr, b) by WGP. However, no depletion was evident for 6 PG’s precursor, G6P (a) and downstream products (c–f) under WGP treatment. These data point to a block at G6PDH, which agreed with WGP’s capacity for M1 repolarization as such block was also evident in human M1–MΦ (Figure 1—figure supplement 1). Moreover, P + W but not Pembro or WGP treatment alone attenuated the synthesis of 13C-glycogen (i) and its precursors 13C6-G1P (g) and -UDPG (h) relative to the Ctl treatment (Figure 5B).

We further tracked the 13C fate through glycolysis, the Krebs cycle, and PPP into nucleotide synthesis, as shown in Figure 5C for pyrimidine and Figure 5D for purine nucleotides and their derivatives. The P + W, but not Pembro or WGP treatment alone, attenuated the 13C incorporation into the ribose (5, d) and pyrimidine base (Base, d) of UTP, which reflected reduced synthesis of the respective 13C-precursors phosphoribosyl pyrophosphate PRPP (5, c) and Asp (a) (Figure 5C). Thus, disruption of the Krebs cycle and the PPP at least in part accounted for decreased UTP synthesis.

Similarly, the incorporation of 13C6-Glc-derived 13C-Gly and -ribose into ATP (b) and GTP (c) via the purine nucleotide synthesis pathway was attenuated by P + W but not by Pembro or WGP alone (Figure 5D). This was shown by the reduced levels of both 13Cbase and 13C5 isotopologues of ATP and GTP in P + W-treated CA OTC. These were opposite to the buildup of the corresponding isotopologues of the precursor IMP (a), which points to a block downstream of IMP, possibly due to the reduced availability of the amino donor Asp (Figure 5C–a).

Altogether, P + W treatment inhibited energy and anabolic metabolism in metastatic NSCLC OTC, while Pembro or WGP treatment alone did not. These results point to a synergistic interaction of Pembro and WGP in disrupting central metabolism in UK2035 tumor tissues.

Pembro+WGP-induced metabolic changes are accompanied by proinflammatory responses and extensive cell death in UK2035 CA lung tissues

The above metabolic disruptions induced by P + W treatment were accompanied by increased release of proinflammatory effectors IL-6, INFα, IL-8, and MIP-1α by CA OTC relative to Ctl, Pembro, or WGP treatment (Figure 6A–D). Although these changes did not reach statistical significance (p-values ≤ 0.05), the trend points to M1-type polarization elicited by P + W treatment.

Pembro+ WGP elicits the release of proinflammatory effectors and cell death in UK2035 CA lung tissues.

CA lung OTCs of UK2035 patient were treated with Ctl (), 40 μg/mL Pembro (), 0.1 mg/mL WGP () (n = 3), or Pembro + WGP combined (P + W ) (n = 2) in the presence of 13C6-Glc for 24 hr. Tissues were subsampled for polar extraction (Figure 5) and formaldehyde fixation while media were analyzed for immune effectors (A–D). The fixed tissues were FFPE-processed, sectioned as 4 µm slices, stained for PCNA/caspase 3 (CAS)/RIP-1, and analyzed by confocal microscopy (E–H), as described in the Experimental. Also shown are the changes in CD8 protein intensity (I) in response to treatments, acquired from RPPA as described in the Experimental. Ctl: control; P: Pembrolizumab; WGP: whole glucan particulate; P + W: Pembrolizumab + WG. Panels (AD) display mean ± sem with n = 3. *p<0.05.

To relate the metabolic and medium effector changes described above to CA tissue status, we multiplex-stained for PCNA, caspase 3 (CAS), and RIP1 on subsamples of the SIRM tissue OTC. Figure 6E–H showed highly heterogenous distribution of PCNA fluorescence (green) for all tissue sections, which presumably reflects the heterogenous distribution of cancer cells. Notably, we observed increased caspase (CAS, orange) and RIP1 (red) fluorescence in P + W-treated tissues (H) versus Ctl- (E), Pembro- (F), or WGP (G)-treated tissues. Also noted was the higher occurrence of CAS+/RIP1+ cells in P + W-treated tissues than Ctl-, Pembro-, and WGP-treated tissues. These data suggest that P + W-treated OTCs were more apoptotic and necrotic than Ctl-, Pembro-, or WGP-treated OTCs. Such compromised tissue status correlated with the extensive attenuation of central metabolism in P + W-treated OTCs from BM NSCLC tumors (Figure 5). This disruption deviated from that seen in Pembro- (Figure 1) or WGP-treated (Figures 2 and 4 in Fan et al., 2016) primary NSCLC OTCs, except for the block at G6PDH. Moreover, a slightly increased expression of CD8 protein in CA tissues under P + W versus Ctl, Pembro, or WGP treatment was observed (Figure 6I), which could reflect T-cell activation in the P + W-treated OTC. Altogether, we surmise that the metabolic attenuation associated with tissue damage overwhelm metabolic stimulation induced by immune activation and that Pembro and WGP synergize in eliciting tissue damage and disrupting tissue metabolism.

Conclusions

Our studies illustrate the value of patient-derived ex vivo tissue cultures in probing in-depth metabolic and cellular sensitivity to immune modulators in the patient’s native TME. We observed metabolic responses to Pembro that are likely to be caused by M1 repolarization of TAM. By coupling with the high-plex DSP analysis, we also saw TMEs likely to be immunoevasive in primary NSCLC tissues of a patient that were overall sensitive to Pembro treatment. We further observed synergism in the tumor-killing action of Pembro and WGP in brain-metastasized NSCLC tissues. Thus, the ex vivo organotypic tissue culture system represents a unique model for interrogating patient’s responses to immunotherapeutics while informing potential resistance mechanism in highly heterogeneous TMEs.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Biological Sample(Homo sapiens)Primary patient NSCLC and matched non cancerous lung tissueFresh thin tissue slices from surgical resectionSee Materials and methods section, this study
Biological Sample(Homo sapiens)NSCLC metastasis to brain patientFresh thin tissue slices from surgical resectionSee Materials and methods section, this study
Biological sample(H. sapiens)Primary monocytesHealthy volunteerFreshly isolated from healthy male volunteer
AntibodyPembrolizumab (humanized mouse anti PD-1 monoclonal)Markey Cancer Center/MerckIF(1:1000), WB (1:1000)
AntibodyAnti-PanCK(mouse monoclonal)InvitrogenSupplementary file 4(1:500)
AntibodyAnti-CD8(mouse monoclonal)Cell SignalingSupplementary file 4(1:200)
AntibodyAnti-CD68(mouse monoclonal)SCBTSupplementary file 4(1:400)
Chemical compound, drugWGPBeta glucanInvivoGenWhole glucan particles
Software, algorithmMNOVA 14.1Mrestlab
OtherDAPI stainInvitrogenD1306(1 µg/mL)
OtherAlexaFluor Tyramide Superboost kits,InvitrogenSee Materials and methods section for detailed use

Patient recruitment and tissue procurement/processing

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Two surgical patients (UK131 and UK2035) were consented for freshly resected tissue specimens under the approved IRB protocol (14–0288-F6A; 13-LUN-94-MCC) of the University of Kentucky (UKy) (Supplementary file 1). Primary cancerous (CA) and surrounding non-cancerous lung tissues of UK131 and brain-metastasized lung cancer tissues of UK2035 were obtained from the surgeons immediately after resection at the operating room. Tissues were transported in DMEM to the laboratory and embedded in 3% low-melting agarose before sectioning into 750 µm thick slices in PBS under sterile conditions using a Krumdieck tissue slicer (Alabama R&D) according to the vendor’s protocol.

Ex vivo organotypic tissue slice culturing, treatments, and extraction

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Tissue slices were cultured for 24 hr in complete DMEM with glucose or Gln replaced by 10 mM 13C6-glucose or 2 mM 13C5,15N2-Gln as described previously (Sellers et al., 2015). The UK131 tissue slices were co-treated with 40 μg/mL Pembro or vehicle while the UK2035 tissue slices were co-treated with 40 μg/mL Pembro, 0.1 mg/mL β-glucan formulated as whole glucan particulates (WGP soluble, InvivoGen), Pembro and WGP combined, or vehicle. Pembro used was freshly prepared by the UKy Pharmacy, the majority of which was used for NSCLC patient therapy in the clinic. WGP was prepared as described previously (Fan et al., 2016). Treatment media were sampled at the start and right before tissue harvest. At harvest, tissues were subsampled for buffered formalin fixing before rapid rinsing three times with excess cold PBS and once with cold nanopore water to remove medium components. Tissues were then flash-frozen in liquid N2 to quench metabolism and stored at −80°C before further processing. The formalin-fixed tissues were paraffin-embedded (FFPE) and sectioned at 4 µm thickness for H&E and mIF staining.

Frozen tissue slices were pulverized into fine particles using a cryo ball mill (SPEX 6775 Freezer/Mill) before simultaneous extraction for polar and non-polar metabolites using the acetonitrile:H2O:chloroform (2:1.5:1, v/v) solvent partitioning method as described previously (Fan et al., 2016). This method also obtained proteins as precipitates, which were solubilized with 62.5 mM Tris + 2 % sodium dodecyl sulfate +1 mM dithiothreitol and denatured at 90°C for 10 min. Protein concentration in the extracts were determined using the BCA (bicinchoninic acid) method (Pierce Chemicals) to calculate the total protein content for normalizing the metabolite content. Medium samples were extracted in 80% cold acetone for polar metabolites as described (Sellers et al., 2015).

Human macrophage culturing, treatment, and extraction

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To generate human MΦ, PBMC from a male volunteer age-matched to the cancer patient (65 years old) were isolated using the RosetteSep human monocyte enrichment cocktail kit (StemCell). Isolated monocytes were then differentiated for 5 days in monocytes differentiation medium (MDM) containing DMEM, 10% FBS, 10 mM glucose, 2 mM Gln, 1× Anti-anti, and 50 ng/mL CSF-1 at 37°C/5% CO2. Seeding density was 12–15 × 106 cells per 10 cm plates. On days 3 of differentiation, media was refreshed with MDM. Naïve MΦ were harvested on day 6 and polarized to either M1 subtype with 100 ng/mL LPS +20 ng/mL IFNγ or to M2 subtype with 20 ng/mL each IL-4+ IL-13 for 2 days. MΦ maintained in MDM served as control (M0). Polarized MΦ were then cultured for 24 hr in MDM medium with glucose or Gln replaced by 10 mM 13C6-glucose or 2 mM 13C5,15N2-Gln. Cells were then washed in cold PBS twice and briefly in nanopure water before simultaneous lysing and quenching of metabolism in cold 100% acetonitrile. Extraction of polar metabolites and proteins in cells and polar metabolites in media were as described above for OTCs.

Polar metabolite analysis

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The polar extracts were split for NMR and/or IC-UHR-FTMS analysis. 1D 1H and 1H (Albers et al., 2008) HSQC NMR analyses were performed on a Bruker Avance III 16.45T spectrometer or an Agilent 14.1T DD2 spectrometer outfitted respectively with a 1.7 mm cryoprobe or a 3 mm coldprobe as described previously (Sellers et al., 2015). IC-UHR-FTMS analysis was done as described previously (Fan et al., 2016) on a Dionex ICS-5000 ion chromatography system coupled to a Thermo Orbitrap Fusion Tribrid Fourier transform mass spectrometer with a mass resolving power of 370,000 at 400 m/z.

Tissue staining

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Four micrometer FFPE sections were stained with hematoxylin and eosin for pathological evaluation. They were also subjected to multiplex immunofluorescence (mIF) staining for protein markers using specific antibodies listed in Supplementary file 4,. FFPE sections were deparaffinized, rehydrated, and subjected to heat-induced epitope retrieval by microwaving in Tris–EDTA buffer (10 mM Tris base, 1 mM EDTA solution, 0.05 % Tween 20, pH 9.0) for 10 min at a sub-boiling temperature. They were stained for multiple markers with primary antibodies from the same species using a Tyramide staining method (Tóth and Mezey, 2007) according to vendor’s protocol (AlexaFluor Tyramide Superboost kits, Invitrogen). Briefly, tissue sections were blocked in 3% hydrogen peroxide for 10 min at room temperature, washed in PBS, blocked in 10% goat serum for 1 hr at room temperature, incubated with appropriately diluted primary antibody overnight at 4°C, washed in PBS, incubated in poly-HRP-conjugated secondary antibody, and washed again in PBS before incubation in a tyramide working solution (e.g. AlexaFluor 647 tyramide) for 10 min followed by immediate application of reaction stop reagent working solution. Tissue sections were then rinsed three times in PBS before microwaving in citrate buffer (10 mM sodium citrate, 0.05% Tween 20, pH 6.0) for 10 min at a sub-boiling temperature and allowed to cool to room temperature while being kept in the citrate buffer. The samples were then washed twice in PBS, blocked in 3% hydrogen peroxide and 10% goat serum again before the application of a second round of primary and secondary antibody, followed by treatment with a second tyramide working solution (e.g. AlexaFluor 594 tyramide). For triple protein marker staining, the process was repeated once more with a third tyramide working solution (e.g. AlexaFluor 488 tyramide). Slides were mounted in Prolong Gold Antifade Mountant with DAPI and dried overnight at 4°C (Thermo Fisher Scientific, P36935) before confocal microscopic imaging.

Digital spatial profiling

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DSP was performed on one slide each of the FFPE sections for control and Pembro-treated UK131 tissue slices using the GeoMx service at Nanostring. Briefly, tissue slides were first stained for cell lineage markers panCK (cancer cells), CD8 (cytotoxic T cells), and CD68 (MΦ) using fluorescent antibodies (Supplementary file 4) and for nuclei using DAPI. Twelve region of interests (ROIs) were then selected for each slide based on the enrichment of panCK, CD8, CD68 and mixed fluorescence. The slides were incubated with antibodies conjugated with photocleavable oligonucleotide barcodes (Oligos) to probe a panel of 58 markers for immune functions and cancer cell type/status. The Oligo barcodes from the chosen ROIs were liberated by laser irradiation and quantified by nCounter (Nanostring). The Oligo count for each marker was normalized by the geometric mean of housekeeping genes (GAPDH, S6, and Histone H3). Differential abundance analysis between Ctl and Pembro treatments was performed by limma (Smyth, 2004) on log2-transformed data. Significantly differentially abundant markers were identified at false discovery rate < 0.05. Additional ratioing of the Oligo counts for different markers was performed to reveal activation status of immune cells.

Statistical analyses

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The unpaired two-tailed t-test was used for comparing means, and the raw p-value was corrected for multiple comparisons using the Benjamini–Hochberg procedure (Benjamini and Hochberg, 1995) when relevant, accepting q < 0.05 as statistically significant.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Excel spreadsheets of data used for tables and figures have been deposited at Dryad.

The following data sets were generated
    1. Lane AN
    2. Fan TWM
    3. Higashi RM
    4. Song H
    5. Daneshmandi S
    6. Mahan AL
    7. Purdom MS
    8. Pittman TA
    9. He D
    10. Wang C
    (2021) Dryad Digital Repository
    Innate immune activation by checkpoint inhibition in patient-derived lung cancer tissues.
    https://doi.org/10.5061/dryad.n5tb2rbwc

References

    1. Maurer M
    2. von Stebut E
    (2004) Macrophage inflammatory protein-1
    The International Journal of Biochemistry & Cell Biology 36:1882–1886.
    https://doi.org/10.1016/j.biocel.2003.10.019
    1. Warburg O
    (1924)
    Is the respiration of cancer cells normal
    Biochem Zeitschr 51:24.

Decision letter

  1. Fabio Malavasi
    Reviewing Editor; University of Torino Medical School, Italy
  2. Tadatsugu Taniguchi
    Senior Editor; Institute of Industrial Science, The University of Tokyo, Japan
  3. Fabio Malavasi
    Reviewer; University of Torino Medical School, Italy
  4. Marco Volante
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Congratulations, we are pleased to inform you that your article, "Innate immune activation by checkpoint inhibition in patient-derived lung cancer tissues", has been accepted for publication in eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor.

Reviewer #1 :

Anti-PD-1 monoclonal antibody therapy can influence the survival of lung cancer patients, but the results are variable and not always satisfactory.

The approach is limited by a general lack of information about the activities induced or intercepted by the antibody on the target tumor and - importantly - within the surrounding environment.

The strength of the paper by Fan and Lane is the adoption of ex-vivo tissue slices from primary and metastatic lung tumors. This makes it possible to track the metabolic reprogramming observed in vivo by analyzing metabolic and morphologic studies. Pembrolizumab was used in combination with the natural immune activator β-glycan. The conclusion of the authors may be justified by the reading of their data, at least from the two patients analyzed.

In my view, the most interesting finding was the metabolic shift induced by Pembro + β-glycan, while the analysis of the effects induced on different cell populations is less strong .

I believe that the strength of the paper could be improved by including or discussing the interplay between PD-1 and CD38 (Chen L et al., Cancer Discovery, 2018).

Comparative analysis could be flanked by additional characterization of the populations observed in the primary and secondary tissue sections (in my view, it is difficult to say that CD25 is a marker of Treg).

Another simple adjunct would be the inclusion of the analysis of the expression of adenosine receptors (Koness J. M. et al, Cells, 2019).

In conclusion, the authors should be commended for their brilliant paper and for their choice of analytical technologies.

Reviewer #2:

The role of immunotherapy rapidly gained a transformative impact in cancer therapy. Nevertheless, modeling the alteration induced by checkpoint inhibitors (e.g. pembrolizumab) is essential to stratify the various degree of response and predict the onset of resistance. To this aim, it is important to develop novel models to integrate existing ones. In this work, Fan et al characterized and refined the tissue slides (originally used by Warburg for his seminal observation in cancer metabolism) by culturing primary tumor sections embedded in agarose, as a tool for modeling the response to checkpoint inhibition while preserving tissue architecture and heterogeneity.

Strength

The model is original and took advantage of a system which is certainly under investigated. Moreover, the techniques used to study heterogeneity as well the metabolomic approach are certainly compelling and provide an accurate indication of the complexity in a tumor model.

Fig4 is very informative providing some insight of the different immune landscape alteration on a slide

Weakness

The potential of this approach is certainly elevated. Nevertheless, the work fails to present clear differences between treatments, for instance:

Fig1 I am not able to see an activation of PPP, besides the increase in S7P, no significant regulation following Pembro treatment were evident.

Fig2 likewise, although the tracing experiment is very elegant, very limited alterations is present besides tumor/non tumor tissue.

Fig3 No biologically relevant alteration is present, plus it is not clear to understand what are the legends presented (C-Glc P-Gln...)?

The rationale behind the use of WGP in combo should be introduced more explicitly, with reference for the relevance in therapy. More data on the response in patients (if not available from the same patient also in similar treatment)

Overall, it is difficult correlate the metabolic rewiring of the macrophages, as reported in Fig S3 (consistent with data already published), with the effects of Pembro-treatment on whole tumor slide.

This work presents various critical points which undermine its significance and makes many of the conclusion non convincing:

The impact of this present work is limited by the use of only two tumor samples.

Only 2 different tissues are not informative: at least these results should have been compared with primary tumor analysis and/or with murine transposition of the same approach and/or with more clinical data from patients.

Moreover, since the authors state in the conclusions paragraph "a patient that was overall sensitive to Pembro treatment", more detailed clinical data should be added to understand if the primary OTCs are really predictive models. Overall, a discussion section is necessary to improve the quality of the manuscript.

Fig5 it is probably a mistake of figure reporting, but the only treatment which appears with great interest (Pembro + WGP) consists of a single data point.

Overall, it is difficult correlate the metabolic rewiring of the macrophages, as reported in Fig S3 (consistent with data already published), with the effects of Pembro-treatment on whole tumor slide. The metabolic profile of the tumor slides cannot represent the metabolic changes of macrophages (5% of density).

In order to improve the quality of the paper and further support their statement the authors should perform an ex vivo treatment of fresh tumour tissues [10.1038/s41598-017-12222-9] and follow with FACS sorting [10.7554/eLife.61980] coupled with metabolomic analysis.

Moreover, a NanoString approach such as nCounter Metabolic Pathways Panel coupled with GeoMx Digital Spatial Profiler could strengthen the metabolomic analysis and be useful to build a metabolic signature predictive for ICIs response.

Tissue slices should be cultured in a more physiological medium (i.e. Plasmax) instead of DMEM.

Reviewer #3:

In the present paper the Authors aimed at investigating the impact of immune checkpoint inhibition in the modulation of cancer cell metabolism and in the impairment of the overall immune tumor microenvironment.

Major strengths of the paper are the innovative model and the study design, aimed at the localization and quantification "in situ" of the set of molecules under investigation.

Major weaknesses are the limited statistical significance of their data and the heterogeneity of the model (poorly characterized tumor typing of both primary tumor and brain metastasis and different treatment approaches in the two models).

Based on strengths and weaknesses above, the Authors partially achieved their objectives, and the conclusions have to be supported in a validation set of experiments. This is of paramount importance for the translational impact of their research. In fact, their model has a clear benefit in helping to understand the impact of immune checkpoint molecules (i.e. pembrolizumab) in the modulation of the immune-to-tumor response, but their data are largely sample-specific and do not take into account the extreme heterogeneity among different cancers.

– The histology of the two tumor samples is not well characterized, despite it is well known that immune-to-tumor response mechanisms are largely heterogeneous among different non-small cell lung cancer histotypes. CK5 expression is not enough to state squamous cell carcinoma (better markers might be p40/p63) and the terminology of the metastatic tumor is not clear if refers to "carcinoid" or "large cell neuroendocrine carcinoma" (much more likely in the presence of brain metastasis). The Authors may come back to the original diagnoses of their samples, with special reference to the metastatic one.

– Statistical significance is reached for a minority of experiments, only, whereas the majority of data are described as significant but with no significant p values. As an example, in figure 1 S7P, only, reached statistical significance whereas in Figure 2 I did not find any statistically significant result. Moreover, as a comment to Figure 3 the Authors state that the majority of investigated molecules did not reach statistical significance, but I could not find the minority with a significance.

– The data reported in Figure 4A and 4B are descriptive, but it is not clear if they refer to the same tumor areas in serial sections (as they seem to be different in the HandE panels). The same applies to Figure 5 E to H. Moreover, a quantitative or semiquantitative estimation of the markers should be performed to sustain significance of variations reported. Data illustrated in Figure 4C are also biased by the different areas evaluated. Although the Authors selected fields with enrichment of tumor cells they loose direct comparison of same tumor areas.

– The Authors might comment of the increased MO content in CA-Pem (figure 4A) and the reduced CD68 expression illustrated in figure 4D.

– It is not clear what the reference in lines 141-142 is related to.

https://doi.org/10.7554/eLife.69578.sa1

Author response

Reviewer #1:

Anti-PD-1 monoclonal antibody therapy can influence the survival of lung cancer patients, but the results are variable and not always satisfactory.

The approach is limited by a general lack of information about the activities induced or intercepted by the antibody on the target tumor and - importantly - within the surrounding environment.

The strength of the paper by Fan and Lane is the adoption of ex-vivo tissue slices from primary and metastatic lung tumors. This makes it possible to track the metabolic reprogramming observed in vivo by analyzing metabolic and morphologic studies. Pembrolizumab was used in combination with the natural immune activator β-glycan. The conclusion of the authors may be justified by the reading of their data, at least from the two patients analyzed.

In my view, the most interesting finding was the metabolic shift induced by Pembro + β-glycan, while the analysis of the effects induced on different cell populations is less strong .

The patient-derived tissue slices maintained the original tumor architecture, which varied throughout the tumor bulk. This heterogeneity is what we sought to demonstrate in this paper. We agree that the approach taken did not directly provide changes of metabolic activity in individual cell types in the tumor microenvironment (TME). However, by relating metabolic/immune functional changes of macrophages in vitro to those observed ex vivo, we can tease apart the overall changes in tumor associated macrophages (TAM) or cancer cells in the native TME in response to immune modulators with the in-depth analysis we opted for in this article.

I believe that the strength of the paper could be improved by including or discussing the interplay between PD-1 and CD38 (Chen L et al., Cancer Discovery, 2018).

This interplay could occur in our case. However, we did not analyze for CD38 in our samples and it would be too speculative to discuss this interplay. However, we observed T cell activation (as evidenced by relatively high granzyme B expression and iCOS/CD4 ratio) in one resistant TME region (ROI 8), so it is unlikely that the resistance was mediated by such interplay in this region.

Comparative analysis could be flanked by additional characterization of the populations observed in the primary and secondary tissue sections (in my view, it is difficult to say that CD25 is a marker of Treg).

It would be ideal to get primary and secondary tumors from the same patient, but to date this has not been possible. We agree that there are multiple markers to define human Treg (CD3+, CD4+, CD25+, CD127+, FoxP3+). Further T cell marker analysis is warranted to substantiate this point but is beyond the scope of this paper.

Another simple adjunct would be the inclusion of the analysis of the expression of adenosine receptors (Koness J. M. et al, Cells, 2019).

We thank the reviewer for this suggestion, and we will include this in our follow-up study.

In conclusion, the authors should be commended for their brilliant paper and for their choice of analytical technologies.

The reviewer’s comment is much appreciated.

Reviewer #2:

The role of immunotherapy rapidly gained a transformative impact in cancer therapy. Nevertheless, modeling the alteration induced by checkpoint inhibitors (e.g. pembrolizumab) is essential to stratify the various degree of response and predict the onset of resistance. To this aim, it is important to develop novel models to integrate existing ones. In this work, Fan et al characterized and refined the tissue slides (originally used by Warburg for his seminal observation in cancer metabolism) by culturing primary tumor sections embedded in agarose, as a tool for modeling the response to checkpoint inhibition while preserving tissue architecture and heterogeneity.

We thank the reviewer for the positive comment. The tissues were sliced in agarose but were incubated as free cultures for metabolic studies.

Strength

The model is original and took advantage of a system which is certainly under investigated. Moreover, the techniques used to study heterogeneity as well the metabolomic approach are certainly compelling and provide an accurate indication of the complexity in a tumor model.

Fig4 is very informative providing some insight of the different immune landscape alteration on a slide

We thank the reviewer for these positive comments.

Weakness

The potential of this approach is certainly elevated. Nevertheless, the work fails to present clear differences between treatments, for instance:

Fig1 I am not able to see an activation of PPP, besides the increase in S7P, no significant regulation following Pembro treatment were evident

As we pointed out in the paper, pembrolizumab-induced changes of individual metabolites in a given pathway such as PPP in Fig. 1 did not reach statistical significance, at least in part due to intrinsic tissue heterogeneity. However, there were consistent changes in the majority of metabolites in the pathways depicted in the tumor (but not in the control) tissues, which points to altered pathway activity.

Fig2 likewise, although the tracing experiment is very elegant, very limited alterations is present besides tumor/non tumor tissue.

Please see response to the comment above. In particular, the tumor OTCs but not the non-cancerous counterparts showed consistent changes in pyruvate and all Krebs cycle metabolites, which points to altered Krebs cycle activity.

Fig3 No biologically relevant alteration is present, plus it is not clear to understand what are the legends presented (C-Glc P-Gln...)?

The data shown are changes in cytokines released into the media, which reflects changes in immune functions and crucial to assessing Pembro’s effect. The abbreviated texts were defined in the Fig. 3’s legend as C-Glc or C-Gln: Ctl + 13C6-Glc or 13C5,15N2-Gln; P-Glc or P-Gln: 40 µg/mL Pembro + 13C6-Glc or 13C5,15N2-Gln.

The rationale behind the use of WGP in combo should be introduced more explicitly, with reference for the relevance in therapy. More data on the response in patients (if not available from the same patient also in similar treatment)

WGP and Pembrolizumab are known to activate different compartments of the immune system, i.e. macrophages and T cells, for which we have provided relevant references in the original text. This was the basis to combine the two treatments to see if they act synergistically in immune activation and tumor killing.

Overall, it is difficult correlate the metabolic rewiring of the macrophages, as reported in Fig S3 (consistent with data already published), with the effects of Pembro-treatment on whole tumor slide.

Figure S3 (now Figure 1- figure supplemental 1) depicted the metabolic reprogramming of isolated human macrophages in response to pro-inflammatory stimuli, which was comparable to that of ex vivo patient-derived OTCs in response to Pembro treatment. This comparison enabled us to discern overall TAM response to Pembro in whole tumor slices. In situ single-cell based SIRM analysis will provide the ultimate proof for this but is beyond current technological limits.

This work presents various critical points which undermine its significance and makes many of the conclusion non convincing:

The impact of this present work is limited by the use of only two tumor samples.

Only 2 different tissues are not informative: at least these results should have been compared with primary tumor analysis and/or with murine transposition of the same approach and/or with more clinical data from patients.

Moreover, since the authors state in the conclusions paragraph "a patient that was overall sensitive to Pembro treatment", more detailed clinical data should be added to understand if the primary OTCs are really predictive models. Overall, a discussion section is necessary to improve the quality of the manuscript.

We agree that more examples will provide additional information on how variably patient tissues can respond to immune modulations, which is part of our ongoing studies. However, each patient’s TME is unique in its own right, which provides insights into individual patient’s drug response without the need for additional comparison. In addition, the native TME and tumor architecture will be lost in the murine explant (PDX) model, which deviates from the purpose of the study. The paragraph cited in the Conclusions section was meant for overall sensitivity of the tumor OTCs, not of the patient. Since Pembro had not been approved for treating primary respectable NSCLC, we do not have the correlation of OTCs response to patient response. This is certainly of interest to pursue in future studies.

Fig5 it is probably a mistake of figure reporting, but the only treatment which appears with great interest (Pembro + WGP) consists of a single data point.

It is not a mistake. We have only one medium sample from the Pembro + WGP treatment available for the cytokine analysis.

Overall, it is difficult correlate the metabolic rewiring of the macrophages, as reported in Fig S3 (consistent with data already published), with the effects of Pembro-treatment on whole tumor slide. The metabolic profile of the tumor slides cannot represent the metabolic changes of macrophages (5% of density).

Please see response to comment 7 on Fig. S3 above. Although we did not quantify the % of macrophages in tumor tissue slices, it is higher than 5% of tumor cells based on the CD68 and panCK staining patterns in Fig. 4C. We would also like to point out that macrophages are much bigger cells than lymphocytes and isolated macrophages show very large metabolic responses to proinflammatory (M1) activation (see Figs. S3 and S4; now Fig.1 – figure supplement 1 and Fig.2 – figure supplement 1). These responses are expected to be “diluted” in patient OTCs by the presence of other cell types, foremost cancer cells. This is consistent with the much less prominent M1-like responses in cancer OTCs.

In order to improve the quality of the paper and further support their statement the authors should perform an ex vivo treatment of fresh tumour tissues [10.1038/s41598-017-12222-9] and follow with FACS sorting [10.7554/eLife.61980] coupled with metabolomic analysis.

We thank the reviewer for the suggestion. However, tissue dissociation followed by FACS sorting is known to alter cell metabolite patterns. We would expect that the tracer enrichment patterns will be even more significantly altered or even lost during the prolonged harvest procedure.

Moreover, a NanoString approach such as nCounter Metabolic Pathways Panel coupled with GeoMx Digital Spatial Profiler could strengthen the metabolomic analysis and be useful to build a metabolic signature predictive for ICIs response.

We agreed that coupling RNA expression analysis with DSP and SIRM analysis would further strengthen the paper. However, we did not have additional OTCs to perform parallel RNA expression analysis, as the fresh patient tissues that we could procure were only sufficient for SIRM study.

Tissue slices should be cultured in a more physiological medium (i.e. Plasmax) instead of DMEM.

We agree that standard media such as DMEM do not represent typical physiological conditions and new medium formulations such as Plasmax more closely mimic metabolite composition of human serum. We will take this into consideration in future studies. In fact, we are exploring different culturing methods to prolong the viability of OTCs such that chronic (e.g. month-long) drug response study can be performed. We would like to point out that the metabolite composition of serum does vary from patient to patient, which makes it difficult to match with a common medium. We also need to supplement certain growth factors to maintain cancer stem cells in OTCs, which are crucial to drug resistance development. Moreover, unlike 2D cell cultures, we expect OTCs to develop localized gradient of nutrients, pH, and waste products such as lactate, which makes complete matching of the metabolite composition of the TME more complex. Suffice to say, we observed metabolic/immune functional responses of OTCs to immune modulators that were consistent with macrophage activation, regardless the use of unphysiological DMEM.

Reviewer #3:

In the present paper the Authors aimed at investigating the impact of immune checkpoint inhibition in the modulation of cancer cell metabolism and in the impairment of the overall immune tumor microenvironment.

We thank the reviewer for the positive comments.

Major strengths of the paper are the innovative model and the study design, aimed at the localization and quantification "in situ" of the set of molecules under investigation.

Major weaknesses are the limited statistical significance of their data and the heterogeneity of the model (poorly characterized tumor typing of both primary tumor and brain metastasis and different treatment approaches in the two models).

Based on strengths and weaknesses above, the Authors partially achieved their objectives, and the conclusions have to be supported in a validation set of experiments. This is of paramount importance for the translational impact of their research. In fact, their model has a clear benefit in helping to understand the impact of immune checkpoint molecules (i.e. pembrolizumab) in the modulation of the immune-to-tumor response, but their data are largely sample-specific and do not take into account the extreme heterogeneity among different cancers.

Please also see response to Reviewer 2’s comment 3. We agree that individual metabolite responses did not reach statistical significance, presumably due to the influence of intrinsic heterogeneity in OTCs. However, metabolites from the same pathway responded in the same direction, which points to altered pathway activity. Patient tissue-specific response to drugs under the influence of tissue heterogeneity is what we sought to investigate. We illustrated this with two patient examples and did not intend to draw conclusion on how NSCLC tissues respond to Pembro or WGP in general. We are continuing this type of study on other patients’ OTCs and will follow reporting many more patient examples to further address the tumor heterogeneity issue.

– The histology of the two tumor samples is not well characterized, despite it is well known that immune-to-tumor response mechanisms are largely heterogeneous among different non-small cell lung cancer histotypes. CK5 expression is not enough to state squamous cell carcinoma (better markers might be p40/p63) and the terminology of the metastatic tumor is not clear if refers to "carcinoid" or "large cell neuroendocrine carcinoma" (much more likely in the presence of brain metastasis). The Authors may come back to the original diagnoses of their samples, with special reference to the metastatic one.

The tumor classification was based on certified clinical diagnosis and histological examination by co-authors Drs. Bocklage and Purdom who are professional pathologists. The histochemical marker selection was according to the pathologist’s suggestion to corroborate with the clinical diagnosis.

– Statistical significance is reached for a minority of experiments, only, whereas the majority of data are described as significant but with no significant p values. As an example, in figure 1 S7P, only, reached statistical significance whereas in Figure 2 I did not find any statistically significant result. Moreover, as a comment to Figure 3 the Authors state that the majority of investigated molecules did not reach statistical significance, but I could not find the minority with a significance.

Please see also response to Comment 2. We did not provide the p values for the majority of metabolites in Figs, 1, 2, and S6 (now Fig. 5) as they were greater than 0.05. We apologize for the oversight of stating “the majority of Pembro effects on CA OTC did not reach statistical significance” for Fig. 3. It should be “none of Pembro effects on CA OTC reach statistical significance”. We have modified the text accordingly.

– The data reported in Figure 4A and 4B are descriptive, but it is not clear if they refer to the same tumor areas in serial sections (as they seem to be different in the HandE panels). The same applies to Figure 5 E to H. Moreover, a quantitative or semiquantitative estimation of the markers should be performed to sustain significance of variations reported. Data illustrated in Figure 4C are also biased by the different areas evaluated. Although the Authors selected fields with enrichment of tumor cells they lose direct comparison of same tumor areas.

The slices in Figs. 4 and 5 (now Fig. 6) were different but prepared from the same block of patient tissue. The distributions of cells varied from slice to slice, reflecting bulk tissue heterogeneity and giving rise to somewhat different staining patterns. The quantitative image analysis for Fig. 4B was unreliable due to large and variable autofluorescence background in different regions of the stained slides. We did obtain quantitative marker data with DSP analysis of panel 4C, which were shown in panels D and E. DSP analysis was not affected by autofluorescence. Choosing different ROIs is how DSP is done such that different ROIs represent different TME. As stated on p. 8 of the revised text, our ROI selections were meant to compare different TMEs, which was enriched in cancer cells, macrophages, lymphocytes or mixed cell types. This is not a bias, rather the selections enabled comparison of different TMEs’ response to Pembro.

– The Authors might comment of the increased MO content in CA-Pem (figure 4A) and the reduced CD68 expression illustrated in figure 4D.

We did not quantify the macrophage content in Fig. 4A, so we are not sure how the reviewer came up with the “increased MO content in CA-Pem”. In fact, by visual examination, the macrophage content may have decreased in CA-Pem

– It is not clear what the reference in lines 141-142 is related to.

The Kashiwagi reference refers to the molecular markers for diagnosing lung neuroendocrine tumors (LNETs).

https://doi.org/10.7554/eLife.69578.sa2

Article and author information

Author details

  1. Teresa WM Fan

    1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, Lexington, United States
    2. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    3. Markey Cancer Center, University of Kentucky, Lexington, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing
    For correspondence
    teresa.fan@uky.edu
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7292-8938
  2. Richard M Higashi

    1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, Lexington, United States
    2. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    3. Markey Cancer Center, University of Kentucky, Lexington, United States
    Contribution
    Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – review and editing
    Competing interests
    none
  3. Huan Song

    1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, Lexington, United States
    2. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    3. Markey Cancer Center, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Writing – review and editing
    Competing interests
    None
  4. Saeed Daneshmandi

    1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, Lexington, United States
    2. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    3. Markey Cancer Center, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  5. Angela L Mahan

    1. Markey Cancer Center, University of Kentucky, Lexington, United States
    2. Departement of Surgery, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  6. Matthew S Purdom

    1. Markey Cancer Center, University of Kentucky, Lexington, United States
    2. Departement of Pathology and Laboratory Medicine, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  7. Therese J Bocklage

    1. Markey Cancer Center, University of Kentucky, Lexington, United States
    2. Departement of Pathology and Laboratory Medicine, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  8. Thomas A Pittman

    Department of Neurosurgery, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  9. Daheng He

    1. Markey Cancer Center, University of Kentucky, Lexington, United States
    2. Department Internal Medicine, University of Kentucky, Lexington, United States
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    None
  10. Chi Wang

    1. Markey Cancer Center, University of Kentucky, Lexington, United States
    2. Department Internal Medicine, University of Kentucky, Lexington, United States
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    None
  11. Andrew N Lane

    1. Center for Environmental and Systems Biochemistry (CESB), University of Kentucky, Lexington, United States
    2. Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, United States
    3. Markey Cancer Center, University of Kentucky, Lexington, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing
    For correspondence
    andrew.lane@uky.edu
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1121-5106

Funding

University of Louisville (1P01CA163223-01A1)

  • Andrew N Lane
  • Teresa WM Fan

University of Louisville (1U24DK097215-01A1)

  • Richard M Higashi
  • Teresa WM Fan
  • Andrew N Lane

University of Louisville (5P20GM121327)

  • Andrew N Lane

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Drs. Jessica Macedo and Qiushi Sun for assistance in the MS analysis, Dr. Salim S El-Amouri for assistance in the human macrophage experiment, Dr. Timothy L Scott for tissue slicing, and Ms. Yan Zhang, Teresa Cassel, Hui Lu for assistance in sample processing/extraction and Dr. Penghui Lin for NMR data collection. This work was supported by grants 1P01CA163223-01A1, 1U24DK097215-01A1, 5P20GM121327 and Shared Resource(s) of the University of Kentucky Markey Cancer Center P30CA177558.

Ethics

Surgical patients were consented for freshly resected tissue specimens under the approved IRB protocol (14-0288-F6A; 13-LUN-94-MCC) of the University of Kentucky (UKy).

Senior Editor

  1. Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan

Reviewing Editor

  1. Fabio Malavasi, University of Torino Medical School, Italy

Reviewers

  1. Fabio Malavasi, University of Torino Medical School, Italy
  2. Marco Volante

Publication history

  1. Received: April 20, 2021
  2. Accepted: July 26, 2021
  3. Accepted Manuscript published: August 18, 2021 (version 1)
  4. Accepted Manuscript updated: August 20, 2021 (version 2)
  5. Accepted Manuscript updated: August 31, 2021 (version 3)
  6. Version of Record published: September 27, 2021 (version 4)

Copyright

© 2021, Fan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Teresa WM Fan
  2. Richard M Higashi
  3. Huan Song
  4. Saeed Daneshmandi
  5. Angela L Mahan
  6. Matthew S Purdom
  7. Therese J Bocklage
  8. Thomas A Pittman
  9. Daheng He
  10. Chi Wang
  11. Andrew N Lane
(2021)
Innate immune activation by checkpoint inhibition in human patient-derived lung cancer tissues
eLife 10:e69578.
https://doi.org/10.7554/eLife.69578

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