| Literature DB >> 35008355 |
Hillary G Pratt1,2, Kayla J Steinberger3, Nicole E Mihalik3, Sascha Ott4, Thomas Whalley5, Barbara Szomolay6, Brian A Boone1,2,3,7, Timothy D Eubank1,2,3,8.
Abstract
Despite modest improvements in survival in recent years, pancreatic adenocarcinoma remains a deadly disease with a 5-year survival rate of only 9%. These poor outcomes are driven by failure of early detection, treatment resistance, and propensity for early metastatic spread. Uncovering innovative therapeutic modalities to target the resistance mechanisms that make pancreatic cancer largely incurable are urgently needed. In this review, we discuss the immune composition of pancreatic tumors, including the counterintuitive fact that there is a significant inflammatory immune infiltrate in pancreatic cancer yet anti-tumor mechanisms are subverted and immune behaviors are suppressed. Here, we emphasize how immune cell interactions generate tumor progression and treatment resistance. We narrow in on tumor macrophage (TAM) spatial arrangement, polarity/function, recruitment, and origin to introduce a concept where interactions with tumor neutrophils (TAN) perpetuate the microenvironment. The sequelae of macrophage and neutrophil activities contributes to tumor remodeling, fibrosis, hypoxia, and progression. We also discuss immune mechanisms driving resistance to standard of care modalities. Finally, we describe a cadre of treatment targets, including those intended to overcome TAM and TAN recruitment and function, to circumvent barriers presented by immune infiltration in pancreatic adenocarcinoma.Entities:
Keywords: PDAC; adenocarcinoma; cancer; hypoxia; immunosuppression; macrophage; metastasis; neutrophil; pancreas
Year: 2021 PMID: 35008355 PMCID: PMC8750413 DOI: 10.3390/cancers14010194
Source DB: PubMed Journal: Cancers (Basel) ISSN: 2072-6694 Impact factor: 6.639
Figure 1Gene expression in tumor type relative to its normal tissue. We used the TCGA TARGET GTEx cohort from the UCSC Xena platform [21], which has 60,498 gene variants in total and combines normalized samples from both TCGA and GTEx. Since the data in [22] has log2(x + 1)-transformed RSEM expected count values, the exponent was taken, and a unity subtracted to obtain the isoform-level estimates. Five TCGA projects (PAAD, pancreatic adenocarcinoma; BRCA, breast invasive carcinoma; SKCM, skin cutaneous melanoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma) containing primary tumor samples were compared using [27]. By taking the overlap of TCGA primary tumor sample IDs and the TCGA TARGET GTEx sample IDs, we obtained PAAD (n = 178), BRCA (n = 1092), SKCM (n = 102), LUAD (n = 512), LUSC (n = 498) samples for each. From the TCGA TARGET GTEx cohort four non-disease tissue sites were extracted: pancreas (n = 155), breast (n = 165), skin (n = 501), lung (n = 247) by searching for the relevant tissue in the “SMTSD” column and RNASEQ keyword in the “SMAFRZE” column of the sample annotation file from the GTEx Portal [26] and then taking the overlap with the TCGA TARGET GTEx sample IDs. The differential expression analysis compared the TCGA data to the GTEx data and was performed using edgeR [23,25]. We define significant genes from the 39 genes of interest if the false discovery rate (FDR) is less than 0.0001 and the log2-fold change is at least ±1. Most significant genes, 33 in total, were identified for the PAAD cohort and less than 26 significant genes for each of the BRCA, SKCM, LUAD, and LUSC projects. MonoMac, bone marrow-recruited monocytes differentiating to macrophages; TEM, TIE2-expressing macrophages; TRM, a priori tissue resident macrophages.
Characterization of macrophage subtypes by function and markers. This table does not include pan-macrophage markers, such as CD11b (Myeloid lineage marker), F4/80 (Murine), and CD68 (Human).
| Macrophage Subtype | Function | Murine Markers | Human Markers |
|---|---|---|---|
| M1-like | Immunosupportive | CD80+ | CD80+ |
| M2-like/TAM | Anti-inflammatory, | CD206+ | CD206+ |
| TEM | Anti-inflammatory, | TIE2+ | TIE2+ |
| MonoMac | Pro-inflammatory, | CCR2+ | CCR2+ |
| TRM | Cloak microlesions, | CX3CR1hi | CX3CR1+ |
Figure 2Hypothesized interactions between macrophages and neutrophils in areas of hypoxia. Both macrophages and neutrophils may be recruited to hypoxic regions within the tumor. Additional details about these interactions are discussed in the text.
Figure 3The timeline of inflammation during development of PDAC. Acute phases of inflammation can be resolved, but the inflammation fails to be resolved in the PDAC environment. This chronic, unresolving inflammation leads to immunosuppression, tissue remodeling, and vessel dysfunction.
Figure 4Recruitment of MonoMacs, TEMs, and neutrophils and proliferation of TRMs causes the unique TME in PDAC. MonoMacs, TEMs, and neutrophils are recruited via CCL2, ANG2, and IL-8, respectively. CX3CL1 causes recruitment and proliferation of TRMs. Together, these cells cause hypoxia, vascular remodeling, fibrosis, tumor progression, metastases, and immunosuppression. The sequalae of macrophage and neutrophil recruitment is further described in the text.
Possible therapeutic strategies that can be used to target macrophages and neutrophils in PDAC.
| Mechanism | Treatment | References |
|---|---|---|
| Macrophage Reprogramming | IFNγ | [ |
| MMP-9 Inhibitor | [ | |
| CD40 agonistic antibody | [ | |
| Nanomicelle PI3k-γ and CSF1-R Inhibition | [ | |
| Pomalidomide | [ | |
| Trabectedin | [ | |
| Macrophage Depletion | Lurbectedin | [ |
| CSF1 Inhibition | [ | |
| CCR2 Antagonism | [ | |
| CCL2 Antibody | [ | |
| Liposomal Clodronate | [ | |
| Neutrophil Reprogramming | IFNβ | [ |
| TGFβ | [ | |
| Neutrophil Depletion | CXCR2 Inhibition | [ |
| Lorlatinib | [ | |
| NETosis Inhibition | Dnase | [ |
| (Hydroxy)Chloroquine | [ | |
| IL-17/IL-17R Blockade | [ | |
| PAD4 Inhibition | [ |