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Renal tumor macrophages linked to recurrence are identified using single-cell protein activity analysis

Posted in Artificial Intelligence in CANCER, Cancer - General, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Immuno-Oncology & Genomics, Metastasis Process, RNA Biology, Cancer and Therapeutics, Single-cell sequencing, tumor microenvironment, tagged cancer metastasis, cancer recurrence, immune cells, learning algorithms, renal cell carcinoma, single-cell RNA sequencing, tumor associated macrophage on May 27, 2021| Leave a Comment »

Renal tumor macrophages linked to recurrence are identified using single-cell protein activity analysis

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

When malignancy returns after a period of remission, it is called a cancer recurrence. After the initial or primary cancer has been treated, this can happen weeks, months, or even years later. The possibility of recurrence is determined by the type of primary cancer. Because small patches of cancer cells might stay in the body after treatment, cancer might reoccur. These cells may multiply and develop large enough to cause symptoms or cause cancer over time. The type of cancer determines when and where cancer recurs. Some malignancies have a predictable recurrence pattern.

Even if primary cancer recurs in a different place of the body, recurrent cancer is designated for the area where it first appeared. If breast cancer recurs distantly in the liver, for example, it is still referred to as breast cancer rather than liver cancer. It’s referred to as metastatic breast cancer by doctors. Despite treatment, many people with kidney cancer eventually develop cancer recurrence and incurable metastatic illness.

The most frequent type of kidney cancer is Renal Cell Carcinoma (RCC). RCC is responsible for over 90% of all kidney malignancies. The appearance of cancer cells when viewed under a microscope helps to recognize the various forms of RCC. Knowing the RCC subtype can help the doctor assess if the cancer is caused by an inherited genetic condition and help to choose the best treatment option. The three most prevalent RCC subtypes are as follows:

Clear cell RCC
Papillary RCC
Chromophobe RCC

Clear Cell RCC (ccRCC) is the most prevalent subtype of RCC. The cells are clear or pale in appearance and are referred to as the clear cell or conventional RCC. Around 70% of people with renal cell cancer have ccRCC. The rate of growth of these cells might be sluggish or rapid. According to the American Society of Clinical Oncology (ASCO), clear cell RCC responds favorably to treatments like immunotherapy and treatments that target specific proteins or genes.

Fig. 1. Clear cell RCC (ccRCC)
Image Source: Moffitt Cancer Center https://moffitt.org/for-healthcare-professionals/clinical-perspectives/clinical-perspectives-story-archive/moffitt-study-seeks-to-inform-improved-treatment-strategies-for-clear-cell-renal-cell-carcinoma/

Researchers at Columbia University’s Vagelos College of Physicians and Surgeons have developed a novel method for identifying which patients are most likely to have cancer relapse following surgery.

The study

Their findings are detailed in a study published in the journal Cell entitled, “Single-Cell Protein Activity Analysis Identifies Recurrence-Associated Renal Tumor Macrophages.” The researchers show that the presence of a previously unknown type of immune cell in kidney tumors can predict who will have cancer recurrence.

Fig. 2. Graphical abstract (Obradovic et al., 2021)
Image Source: https://marlin-prod.literatumonline.com/cms/attachment/a1208337-6b5b-4ef3-996d-227eb2536553/fx1_lrg.jpg

According to co-senior author Charles Drake, MD, PhD, adjunct professor of medicine at Columbia University Vagelos College of Physicians and Surgeons and the Herbert Irving Comprehensive Cancer Center,

the findings imply that the existence of these cells could be used to identify individuals at high risk of disease recurrence following surgery who may be candidates for more aggressive therapy.

As Aleksandar Obradovic, an MD/PhD student at Columbia University Vagelos College of Physicians and Surgeons and the study’s co-first author, put it,

it’s like looking down over Manhattan and seeing that enormous numbers of people from all over travel into the city every morning. We need deeper details to understand how these different commuters engage with Manhattan residents: who are they, what do they enjoy, where do they go, and what are they doing?

To learn more about the immune cells that invade kidney cancers, the researchers employed single-cell RNA sequencing. Obradovic remarked,

In many investigations, single-cell RNA sequencing misses up to 90% of gene activity, a phenomenon known as gene dropout.

The researchers next tackled gene dropout by designing a prediction algorithm that can identify which genes are active based on the expression of other genes in the same family. “Even when a lot of data is absent owing to dropout, we have enough evidence to estimate the activity of the upstream regulator gene,” Obradovic explained. “It’s like when playing ‘Wheel of Fortune,’ because I can generally figure out what’s on the board even if most of the letters are missing.”

The meta-VIPER algorithm is based on the VIPER algorithm, which was developed in Andrea Califano’s group. Califano is the head of Herbert Irving Comprehensive Cancer Center’s JP Sulzberger Columbia Genome Center and the Clyde and Helen Wu professor of chemistry and systems biology. The researchers believe that by including meta-VIPER, they will be able to reliably detect the activity of 70% to 80% of all regulatory genes in each cell, eliminating cell-to-cell dropout.

Using these two methods, the researchers were able to examine 200,000 tumor cells and normal cells in surrounding tissues from eleven patients with ccRCC who underwent surgery at Columbia’s urology department.

The researchers discovered a unique subpopulation of immune cells that can only be found in tumors and is linked to disease relapse after initial treatment. The top genes that control the activity of these immune cells were discovered through the VIPER analysis. This “signature” was validated in the second set of patient data obtained through a collaboration with Vanderbilt University researchers; in this second set of over 150 patients, the signature strongly predicted recurrence.

These findings raise the intriguing possibility that these macrophages are not only markers of more risky disease, but may also be responsible for the disease’s recurrence and progression,” Obradovic said, adding that targeting these cells could improve clinical outcomes

Drake said,

Our research shows that when the two techniques are combined, they are extremely effective at characterizing cells within a tumor and in surrounding tissues, and they should have a wide range of applications, even beyond cancer research.

Main Source

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages

https://www.cell.com/cell/fulltext/S0092-8674(21)00573-0

Tumor Associated Macrophages: The Double-Edged Sword Resolved?

Posted in Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Immuno-Oncology & Genomics, tagged Adaptive immune system, Biology, breast cancer, Cancer - General, Cancer immunology, Cell Biology, Conditions and Diseases, gene expression, genomics, macrophages, modulation of tumor tolerance, research, Stem cell, tumor associated macrophage, tumor microenvironment, Weill Cornell on September 15, 2015| Leave a Comment »

Tumor Associated Macrophages: The Double-Edged Sword Resolved?

Writer/Curator: Stephen J. Williams, Ph.D.

UPDATED 10/04/2021 TAMs Enhance Tumor Hypoxia and Aerobic Glycolysis

Cell-based immunity is vital for our defense against pathologic insult but recent evidence has shown the role of cell-based immunity, especially macrophages to play an important role in both the development and hindrance of tumor growth, including role in ovarian, hematologic cancers, melanoma, and breast cancer. In the past half century, new immunological concepts of cancer initiation and progression have emerged, including the importance of the harnessing the immune system as a potential anti-cancer strategy. However, as our knowledge of the immune system and tumor biology has grown, the field has realized an immunological conundrum: how can an immune system act to both prevent tumor growth and promote the tumor’s growth?

As discussed in the lower section of this post, authors of a paper in the journal Science show how different populations of tumor-associated macrophages (TAMs) may exert both positive and negative effects on tumor cells, producing a sort of ying-yang war between the tumor and the immune system.

The Immune System: Brief Overview and Role in Cancer

Figure. Cell lineage of the immune system. A description of the different cell types can be found here.

Histologic evaluation of multiple tumor types, especially solid tumors, reveal the infiltration of diverse immunological cell types, including myeloid and lymphoid cell lineages, such as macrophages and NK, T cell and B cells respectively.

The immunological conundrum

Figure. Potential inflammatory signaling pathways in breast cancer stem cells.
Breast cancer stem cells may be regulated by chemokine- and/or cytokine-mediated inflammatory signaling in an autocrine or paracrine manner. (from University of Tokyo at http://www.ims.u-tokyo.ac.jp/system-seimei/en/research2_e.htm)

Role of Tumor Associated Macrophages

There are conflicting reports as to the functional consequence of these infiltrating tumor-associated macrophages (TAMs). TAMs have been shown to secrete mediators such as interleukins and cytokines in a paracrine manner such as CCL2, IL10 and TGFβ. In certain instances these cytokines and mediators actually promote the growth of the surrounding tumors.

Figure. TAMs can be divided into subpopulations with distinctive functions and secretogogues.

For Further Reference

Tumor-associated macrophages and the profile of inflammatory cytokines in oral squamous cell carcinoma. http://www.ncbi.nlm.nih.gov/pubmed/23089461 anti-inflamm IL10 and TGFB

Tumor-associated macrophage-derived IL-6 and IL-8 enhance invasive activity of LoVo cells induced by PRL-3 in a KCNN4 channel-dependent manner

Figure 2: TAM functions in tumor progression. Tumor cells and stromal cells, which produce a series of chemokines and growth factors, induce monocytes to differentiate into macrophages. In the tumor, most macrophages are M2-like, and they express some cytokines, chemokines, and proteases, which promote tumor angiogenesis, metastasis, and immunosuppression. From Macrophages in Tumor Microenvironments and the Progression of Tumors

Macrophages integrate metabolic and environmental signals to promote tumor growth. Area within dotted rectangle indicates proposed mechanisms of action. ARG, arginase; HIF, hypoxia-inducible factor; MCT, monocarboxylate transporter; NADH, nicotine adenine dinucleotide, reduced; PKM2, M2 isoform of pyruvate kinase; VEGF, vascular endothelial growth factor from Tumor cells hijack macrophages via lactic acid adapted from Colegio OR, Chu N-Q, Szabo AL, Chu T, Rhebergen AM, Jairam V et al. Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature (e-pub ahead of print 13 July 2014; doi:10.1038/nature13490). | Article |

Depletion of M2-Like Tumor-Associated Macrophages Delays Cutaneous T-Cell Lymphoma Development In Vivo

Targeting tumor-associated macrophages in an orthotopic murine model of diffuse malignant mesothelioma

Crosstalk between colon cancer cells and macrophages via inflammatory mediators and CD47 promotes tumour cell migration

Tumor-Associated Macrophages Regulate Murine Breast Cancer Stem Cells Through a Novel Paracrine EGFR/Stat3/Sox-2 Signaling Pathway

Science Paper: Different Populations of TAMS Have Different Tumor Effects

The cellular and molecular origin of tumor-associated macrophages Eric G. Pamer ¹ Ruth A. Franklin ¹,², Will Liao ³, Abira Sarkar ¹, Myoungjoo V. Kim ¹,², Michael R. Bivona ¹, Kang Liu ⁴, Ming O. Li ¹, Science 23 May 2014: Vol. 344 no. 6186 pp. 921-925

A recent Science paper from Cornel has investigated the origin, function, and characterization of TAMs on breast cancer growth. In summary, their efforts and research suggest different populations of TAMs with varied tumorigenic effects, a finding which may help explain the immunologic conundrum with respect to solid tumors.

The authors characterized the infiltrating immune cell types in a MMTV-PyMt model of breast cancer.

The MMTV-PyMt mouse breast cancer model:

is a transgenic model where mammary gland expression of the polyoma middle T antigen (PyMT) is driven by the Mouse Mammary Tumor Virus promoter (MMTV).

For a review of mouse models of breast cancer please see

Mouse models of breast cancer metastasis. Anna Fantozzi¹ and Gerhard Christofori. Breast Cancer Res. 2006; 8(4): 212.

Results

1. Macrophages constitute the predominate myeloid cell population in MMTV-PyMT mammary tumors

Tumor infiltrating immune cells included

Myeloid cells comprised 50% of CD45+ infiltrating leukocytes.
The CD45 antigen, also known as Protein tyrosine phosphatase, receptor type, C (PTPRC) is an enzyme that, in humans, is encoded by the PTPRC gene, and acts as a regulator of B and T-lymphocytes.
Authors noted three types of cells classified as Type I, II, and III based on

Cell morphology
Major histocompatibility complex

Infiltrating monocytes and neutrophils
Cells with dendritic and macrophage markers

2. TAMS differentiate from CCR2+ inflammatory monocytes

To determine whether Ly6C⁺CCR2⁺ inflammatory monocytes contributed to TAMs and MTMs, authors crossed PyMT mice to Ccr2^−/− mice and found MTMs (mammary tumor macrophages) were significantly reduced in Ccr2^−/− PyMT mice, implying that MTMs are constitutively repopulated by inflammatory monocytes
To determine whether inflammatory monocytes were required for TAM maintenance, we generated CCR2^DTR PyMT mice expressing diphtheria toxin receptor (DTR) under control of the Ccr2 locus DT treatment resulted in 96% depletion of tumor-associated monocytes compared to 80% depletion in Ccr2^−/− mice
To investigate whether monocytes could differentiate into TAMs in vivo, we transferred CCR2⁺ bone marrow cells isolated from CCR2^GFP reporter mice into congenically marked CCR2^DTR PyMT mice depleted of endogenous monocytes, we observed transferred cells in developing tumors demonstrate that tumor growth induces the differentiation of CCR2⁺ monocytes into TAMs.

3. TAMs are phenotypically distinct from AAMs (M2 or alternatively activated macrophages)

Gene-expression profiling revealed the integrin CD11b (Itgam) was expressed at lower levels in TAMs than in MTMs while several other integrins and the integrin receptor Vcam1 were up-regulated in TAMs
AM population did not express AAM markers such as Ym1, Fizz1, and Mrc1; instead, MTMs more closely resembled AAMs. The authors detected Vcam1 up-regulation on TAMs as a late differentiation event

4. RBPJ-dependent TAMs modulate the adaptive immune response

In DCs, canonical Notch signaling mediated by the key transcriptional regulator RBPJ controls lineage commitment and terminal differentiation. To explore whether Notch signaling played a role in TAM differentiation, authors used CD11c^cre mice that efficiently deleted floxed DNA sequences to a greater extent in TAMs than MTMs, but not in monocytes or neutrophils (fig. S14). CD11c^creRbpj^fl/fl PyMT mice exhibited a selective loss of MHCII^hiCD11b^lo TAMs ( 4A). However, a MHCII^hiCD11b^hi population still remained
Transcriptional profiling comparing this population to WT TAMs confirmed a loss of the Notch-dependent program in RBPJ-deficient cells revealing that in the absence of RBPJ, inflammatory monocytes are unable to terminally differentiate into TAMs.

UPDATED 10/04/2021 TAMs Enhance Tumor Hypoxia and Aerobic Glycolysis

Tumor-Associated Macrophages Enhance Tumor Hypoxia and Aerobic Glycolysis

From:

Tumor-Associated Macrophages Enhance Tumor Hypoxia and Aerobic Glycolysis

Hoibin Jeong, Sehui Kim, Beom-Ju Hong, Chan-Ju Lee, Young-Eun Kim, Seoyeon Bok, Jung-Min Oh, Seung-Hee Gwak, Min Young Yoo, Min Sun Lee, Seock-Jin Chung, Joan Defrêne, Philippe Tessier, Martin Pelletier, Hyeongrin Jeon, Tae-Young Roh, Bumju Kim, Ki Hean Kim, Ji Hyeon Ju, Sungjee Kim, Yoon-Jin Lee, Dong-Wan Kim, Il Han Kim, Hak Jae Kim, Jong-Wan Park, Yun-Sang Lee, Jae Sung Lee, Gi Jeong Cheon, Irving L. Weissman, Doo Hyun Chung, Yoon Kyung Jeon and G-One Ahn

Cancer Res February 15 2019 (79) (4) 795-806; DOI: 10.1158/0008-5472.CAN-18-2545

Abstract

Tumor hypoxia and aerobic glycolysis are well-known resistance factors for anticancer therapies. Here, we demonstrate that tumor-associated macrophages (TAM) enhance tumor hypoxia and aerobic glycolysis in mice subcutaneous tumors and in patients with non–small cell lung cancer (NSCLC). We found a strong correlation between CD68 TAM immunostaining and PET ¹⁸fluoro-deoxyglucose (FDG) uptake in 98 matched tumors of patients with NSCLC. We also observed a significant correlation between CD68 and glycolytic gene signatures in 513 patients with NSCLC from The Cancer Genome Atlas database. TAM secreted TNFα to promote tumor cell glycolysis, whereas increased AMP-activated protein kinase and peroxisome proliferator-activated receptor gamma coactivator 1-alpha in TAM facilitated tumor hypoxia. Depletion of TAM by clodronate was sufficient to abrogate aerobic glycolysis and tumor hypoxia, thereby improving tumor response to anticancer therapies. TAM depletion led to a significant increase in programmed death-ligand 1 (PD-L1) expression in aerobic cancer cells as well as T-cell infiltration in tumors, resulting in antitumor efficacy by PD-L1 antibodies, which were otherwise completely ineffective. These data suggest that TAM can significantly alter tumor metabolism, further complicating tumor response to anticancer therapies, including immunotherapy.

Significance: These findings show that tumor-associated macrophages can significantly modulate tumor metabolism, hindering the efficacy of anticancer therapies, including anti-PD-L1 immunotherapy.

Introduction

Tumor hypoxia and glycolysis have long been recognized as major resistance factors contributing to failures of chemo- and radiotherapy (1, 2). Traditionally, tumor hypoxia is known to occur by two mechanisms: chronic or acute hypoxia (2). Chronic hypoxia occurs as a result of rapid proliferation of cancer cells and hence being constantly forced away from blood vessels beyond the oxygen diffusion distance of approximately 150 μm (2). Acute hypoxia on the other hand occurs by a temporary cessation of the blood flow due to highly disorganized tumor vasculature (2). Regardless of the mechanism, tumor hypoxia has been extensively documented for their contribution to resistance to all anticancer therapies including chemotherapy (2), surgery (3), radiotherapy (2), and recently immunotherapy (4).

Aerobic glycolysis, also known as Warburg effect, is a phenomenon whereby many types of tumors exhibit a preference of glucose over the oxygen for their energy substrate (5), and this has allowed us to track solid tumors in patients with PET using ¹⁸fluoro-deoxyglucose (FDG) radioactive tracer (6). Although several mechanisms for Warburg effect have been suggested including mitochondrial defects, adaptation to hypoxia [hence activation of hypoxia-inducible factor (HIF)], and oncogenic signals such as MYC and RAS (7), the exact mechanism is still controversial. Tumor glycolysis has also been reported to influence the therapy outcome (8). Preclinical studies have suggested that glycolysis can increase DNA repair enzyme expressions including Rad51 and Ku70, which can facilitate radiation-induced DNA double-strand break repair (9). Lactate, a major byproduct of glycolysis, has recently been shown to be utilized as a fuel source for oxidative phosphorylation in nearby cancer cells (10), which can promote the tumor recurrence following anticancer therapies.

Tumor-associated macrophages (TAM) are bone marrow–derived immune cells recruited to tumors and have been extensively reported for their protumoral role (11). Recruited to tumors by various tumor-secreting factors including stromal cell–derived factor-1 (SDF1; ref. 12), VEGF (13), semaphorin 3A (14), and colony-stimulating growth factor-1 (CSF1; ref. 15), TAMs have been shown to produce various growth factors and proteases necessary for tumor survival (11) or immunosuppressive cytokines inhibiting antitumor immune responses (16). Macrophages in general are known to be polarized to either classically activated M1 macrophages or alternatively activated M2 phenotype depending on the cytokine milieu in which they are exposed (17). Bacterial-derived products such as lipopolysaccharide have been shown to polarize macrophages toward M1 phenotype (17), while parasite-associated signals such as IL4 and IL13 can lead to M2-polarized macrophages with increased tissue repair abilities (17). It has been suggested that TAMs are M2-like, although various subpopulations of TAM have been also identified including TIE2-positive macrophages (18), programmed cell death protein-1 (PD-1)–expressing TAM (19), and C-C chemokine receptor type-2 (CCR2)–expressing TAM (20).

In this study, we demonstrate clinically and preclinically that TAMs are a novel contributor to tumor hypoxia and aerobic glycolysis by competing oxygen and glucose with cancer cells. We further observed that TAM can significantly interfere with T-cell infiltration thereby masking programmed death-ligand 1 (PD-L1) expression in the tumors. We believe that our results have an important clinical implication such that patients with high infiltration of TAM in their tumors may poorly respond to all anticancer therapies, including the latest immunotherapy.

Figure 1.

Strong correlations between TAM infiltration and glycolysis in patients with NSCLC. A, Representative PET/CT images for FDG uptake (top) and immunostaining of CD68 (bottom) from paired tumors of patients with NSCLC. Top, yellow circles, location of tumors. Bottom, red arrowheads, CD68-positive TAM. Scale bar, 100 μm. B, Correlation between glycolysis and CD68-positive TAM in 98 patients with NSCLC paired results as in A. Glycolysis was analyzed as FDG maximal standardized uptake value (FDG SUV_max; left) or 40% total lesion glycolysis (TLG; right). C, FDG SUV_max values for CD68^low (n = 49) or CD68^Hi (n = 49) NSCLC tumors. D, Subgroup analyses of FDG uptake in adenocarcinomas (n = 48; left) or squamous cell carcinomas (n = 50; right) of NSCLC. *, P < 0.05; ***, P < 0.001 in C and D as determined by the Student t test. Data are the mean ± SEM. E, TCGA analysis between CD68 and SLC2A1 (left) or HK2 (right) in 513 patients with adenocarcinoma NSCLC. P values are indicated in each plot.

TAMs make tumors more glycolytic

Figure 2.

TAMs make tumors more glycolytic. A, Left, PET/MRI images for FDG uptake in LLC tumors in mice before (D0, top) and after (D2, bottom) Veh or Clod treatment. Yellow circles, tumors. Right, T2-weighted MR images of LLC tumors treated with Veh or Clod pre (top)- or post (bottom)- contrast. Red arrowheads in Veh tumor, ferumoxytol-labeled TAM. B, FDG uptake SUV_max in A. **, P < 0.01, determined by two-way ANOVA. C, FACS plot indicating Hoechst^brightKeratin⁺ (red boxes) population of cells sorted as aerobic cancer cells. D, Fold changes in gene expression in FACS-sorted aerobic cancer cells from LLC tumors treated with Clod or Veh. E, Glucose uptake (left) and lactate production (right) from the sorted aerobic cancer cells as in C. Data in D and E are the mean ± SEM from at least triplicate samples. *, P < 0.05; ***, P < 0.001 by the Student t test. F, Western blot of FACS-sorted aerobic cancer cells in C for GLUT1. β-Actin was used as the loading control. G, Oxygen consumption kinetics in FACS-sorted aerobic cancer cells as described in C. H, LLC tumor growth in mice treated with Veh, Clod, Veh + metformin (Veh + Met), or Clod + metformin (Clod + Met). *, P < 0.05; **, P < 0.01; ***, P < 0.001, determined by two-way ANOVA. I, LLC tumor growth in mice treated with Veh, Clod, Veh + 2-DG, or Clod + 2-DG. Data in H and I are the mean ± SEM, with number of animals indicated in the graphs.

TAMs secrete TNFα to promote tumor glycolysis

Figure 3.

Macrophages secrete TNFα to facilitate glycolysis in cancer cells. A, Gene expression changes in LLC cocultured with (LLC+BMDM) or without (LLC) BMDM. Data are the mean ± SEM from at least triplicate determinations. B, Glucose uptake (left) and lactate production (right) in LLC cocultured with or without BMDM. Data are the mean ± SEM for triplicate samples per group. **, P < 0.01 by Student t test. C, Glucose uptake in LLC cultured alone, cocultured with BMDM, or cocultured with BMDM with glucose added back to the LLC compartment of the coculture system. Data are the mean ± SEM for n = 4 replicates per group. *, P < 0.05; **, P < 0.01 by one-way ANOVA. D, Antibody cytokine arrays in the supernatant obtained from BMDM culture with (BMDM+LLC) or without (BMDM) LLC. Red boxes indicate those cytokines whose expressions were increased in BMDM cocultured with LLC compared with BMDM alone. Blue box, CXCL1, a cytokine produced by LLC cancer cells themselves (Supplementary Fig. S2D). E, Luminex cytokine assays for TNFα in the supernatant from culture media, LLC alone, BMDM alone, or BMDM cocultured with LLC. Data are the mean ± SEM for n = 3 replicates per group. ***, P < 0.001 by one-way ANOVA. F, Glucose uptake in LLC alone (none), LLC cocultured with BMDM (+BMDM), or LLC treated with TNFα (+TNFα) or with IFNγ (+IFNγ). Data are the mean ± SEM from n = 3 samples per group. **, P < 0.01; ***, P < 0.001 by one-way ANOVA. G, Western blot for LLC cells treated with increasing concentrations of recombinant TNFα protein for GLUT1, HK2, or PGC-1α. β-Actin was used as the loading control. H, TNFα concentrations in the supernatant from LLC cultured with (+BMDM) or without (alone) BMDM, or in BMDM cultured with (+LLC) or without (alone) LLC, measured by ELISA. BD, below the detection limit. **, P < 0.01 by Student t test. I, Immunostaining of TNFα (red) and CD68 (green) in LLC tumors grown in mice. Nuclei are shown in blue with DAPI counterstaining. The inset shows magnified regions where indicated with the asterisk (*). White arrowheads, CD68-positive TAM-expressing TNFα. Scale bar, 100 μm. J, TNFα concentrations measured by ELISA in the supernatant from CD11b and F4/80 double-positive TAM sorted by FACS. Data are the mean ± SEM from triplicate determinations. ***, P < 0.001, determined by one-way ANOVA. K, TCGA analysis of clinical correlations between CD68 and TNF (left) or between TNF and HK2 (right) in 513 patients with adenocarcinoma NSCLC. P values are indicated in each plot.

TAMs exacerbate tumor hypoxia

Figure 4.

TAMs directly contribute to tumor hypoxia. A, Immunostaining of LLC tumors grown in mice for TAM by using S100A8 (red) and hypoxia by using pimonidazole (PIMO; green) antibodies. Nuclei are shown in blue with DAPI counterstaining. B, FACS analysis demonstrating that CD11b and F4/80 double-positive TAMs are pimonidazole-positive. C, Gene expression in CD11b and F4/80 double-positive TAM isolated from LLC tumors compared with those in cultured BMDM. Data are the mean ± SEM from triplicate determinations. D, Two-photon microscopy images of the dorsal window chamber whereby 5 × HRE-GFP–expressing LLC tumors had been implanted. Images were taken at 24 hours after a single intratumoral injection of PBS (+PBS) or PBS containing FACS-sorted TAM (+TAM). Scale bars in A and D, 100 μm. E, Representative FACS plots demonstrating Hoechst^brightKeratin⁺ as aerobic tumor cells (red boxes) and Hoechst^dimKeratin⁺ as hypoxic tumor cells in LLC tumors grown in mice treated with Veh or Clod. F, Quantification of aerobic or hypoxic tumor cells in E. Data are the mean ± SEM for n = 6 mice per group. *, P < 0.05 by Student t test. G, TCGA analysis between CD68 and HIF1A in 513 patients with adenocarcinoma NSCLC. P value is indicated in the graph. H, Growth of LLC tumor treated with Veh or Clod immediately prior to a single dose of 20 Gy ionizing irradiation. *, P < 0.05 by two-way ANOVA.