Sonia Marsilio is an Associate Product Manager at Enzo. She explains how research tools enable scientists to accelerate immunotherapy.
The growing, continuously evolving arsenal of cancer immunotherapies has changed the treatment landscape of many tumours. Dominating the space are the CAR T cell therapies, with CD19, BCMA, and CD22 as the top targets for haematological malignancies and undisclosed tumour-associated antigen (TAA) for solid tumours. Other immunotherapies include immunomodulators (eg. immune checkpoint inhibitors_ICIs and cytokines), targeted therapies (eg. antibody-drug conjugate s(ADCs) and bi-specific T cell-engaging antibodies (BiTEs), cancer vaccines and oncolytic viruses.
Despite the remarkable success of ICIs in patients with advanced metastatic cancer, several challenges exist: Why do some people respond to immunotherapy but not others? How to turn non-immunogenic ‘cold’ tumours ‘hot’? What immune and non-immune components contribute to immune suppression?
A promising strategy to increase the immunogenicity of cancers is to combine ICIs with conventional cancer treatments such as chemotherapy and radiotherapy as the latter induce the so-called immunogenic cell death (ICD) by releasing tumour antigens. ICD represents a way to deliver danger-associated molecular patterns (DAMPs, e.g. calreticulin, ATP, HMGB1, HSP70/90) to the tumour microenvironment (TME) leading to the maturation of dendritic cells and activation of adaptive immune response against tumours.
In a recent 2020 book chapter1, Kasikova et al. provide a detailed description of methods for the quantification of CALR exposure in the TME of cancer patients by flow cytometry (FC) and immunohistochemistry (IHC). In their study, they used a CALR antibody to detect calreticulin translocation to the cell-surface of dying tumour cells that undergo ICD.
Soluble factors and membrane-bound proteins within the tumour and its microenvironment can suppress T-cell activation, promote T-cell exhaustion, or activate regulatory T cells (Tregs). Upregulation of immune checkpoint molecules on various cell types present in the TME is a major mechanism to suppress anti-tumour immunity. Additionally, chemokines such as CCL2, CCL5 or CXCL12 in the TME can recruit MDSC (Myeloid Derived Suppressor Cells) and Tregs to the tumour. The most important immunosuppressive factors include TGF-β, IL-10, CD73-derived adenosine, VEGF and prostaglandins.
In a recent publication in the journal of Cancer Immunology Research (2018)2, Wing et al. used a LAG-3 (human) monoclonal antibody to monitor T-cell activation status in xenograft mouse models to test new combination therapies. In their study, the authors tested the hypothesis that combining CAR T cells targeting folate receptor (FR-α) with an oncolytic adenovirus expressing a BiTE (Bispecific T cell engagers) targeting another antigen (EGFR) could improve CART-cell therapy. Treatment with OAd-EGFR-BiTE was able to increase FR-α-CART cell killing, proliferation, and IFNγ production in vitro. Also, dual treatment enhanced anti-tumour efficacy due to improved T-cell activation in xenograft mouse models, as assessed by flow cytometry at 15 days post-injection.
An important feature of many tumours is their metabolic adaptation to a low-oxygen environment (hypoxia) that is coordinated by hypoxia-inducible factor–1 (HIF-1). Some tumours display constitutive activation of HIF-1 under normoxic conditions through a variety of mechanisms, including hyperactivation of mTORC1, loss of von Hippel–Lindau, and accumulation of reactive oxygen species (ROS). In Int J Nanomedicine (2019)3, Ma et al. investigated the photodynamic property of light-enhanced hypoxia-responsive nanoparticles by assessing ROS level using a ROS/hypoxia Kit.
In JCI Insight (2019)4, Lu et al. targeted the glucose pathway to overcoming drug resistance in head and neck squamous cell carcinoma (HNSCC). In particular, inhibition of the mitochondrial enzyme pyruvate dehydrogenase kinase-1 (PDK-1), that is over-expressed in HNSCC, in combination with EGFR-blocking, resulted in ROS overproduction and apoptosis. The scientist used an anti-PDK1 and total ROS detection kit in their study.
In Cell Death Dis (2018)5, Zielke et al. screened a library of known autophagy modulators to identify novel ACD (Autophagic Cell Death)-inducing drugs in apoptosis-resistant glioblastoma cells. Of note, loperamide and pimozide induced autophagy that correlated with dephosphorylation and inactivation of mTORC1.
The metabolite composition of the TME is shaped by stromal and immune cells, each with unique metabolic profiles, dependencies and vulnerabilities compared with those of cancer cells. Competition for amino acids, including arginine and tryptophan, between T cells and cancer cells can suppress anti-tumour immunity. The balance of this competition has been linked to the expression and activity of amino acid transporters and key metabolic enzymes. Many tumours, including melanoma, pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, show high expression of the enzyme indoleamine 2,3-dioxygenase (IDO), that is crucial for the catabolism of tryptophan. IDO activity increases tryptophan uptake from the TME and produces kynurenine, an inhibitor of tryptophan import, thus suppressing T cell activation and increasing the number of Tregs6. Therefore, identifying unique metabolic vulnerabilities of T cells and cancer cells could inform more selective therapeutic strategies for patients with unmet needs.
Volume 23, Issue 1 – Winter 2021/22
About the author
Sonia Marsilio, Ph.D is an Associate Product Manager at Enzo. She works with the commercialisation team to develop and support needed life science products. Before joining Enzo, she worked as a research scientist across academia and the pharmaceutical industry. She earned her M.Sc. in Biology and Ph.D. in Genetics and Molecular Biology from La Sapienza University in Rome.
References
- https://pubmed.ncbi.nlm.nih.gov/32000894/
- https://pubmed.ncbi.nlm.nih.gov/29588319/
- https://pubmed.ncbi.nlm.nih.gov/31417257/
- https://pubmed.ncbi.nlm.nih.gov/31578313/
- https://pubmed.ncbi.nlm.nih.gov/30250198/
- https://pubmed.ncbi.nlm.nih.gov/33398194/
