Radiation Responses in Tumour Microenvironment

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1		Niall Byrne	+ 2071 word(s)	2071	2021-02-07 04:09:34	\|
2	format correction	Peter Tang	Meta information modification	2071	2021-03-06 13:50:22	\|

This entry is adapted from the peer-reviewed paper 10.3390/jpm11010053

Radiotherapy (RT) is a primary treatment modality for a number of cancers, offering potentially curative outcomes. Despite its success, tumour cells can become resistant to RT, leading to disease recurrence. Components of the tumour microenvironment (TME) likely play an integral role in managing RT success or failure including infiltrating immune cells, the tumour vasculature and stroma. Furthermore, genomic profiling of the TME could identify predictive biomarkers or gene signatures indicative of RT response.

biomarkers immune infiltrate radiotherapy stroma tumour microenvironment radioresistance precision radiotherapy

1. Introduction

Radiotherapy (RT) is a primary treatment modality for a number of cancers, offering potentially curative outcomes^[1]. Radiation treatment modalities have significantly improved over the last two decades with the introduction of advanced techniques including stereotactic radiotherapy (SRT) and enhanced imaging methodologies to improve the precision of RT delivery, thus limiting damage to healthy tissue. However, despite these advancements, resistance to radiotherapy still occurs, resulting in disease recurrence. Characterisation of radioresistance has traditionally focused on the effects of RT on tumour cells, overlooking the impact on supporting stromal and immune cells that make up the tumour microenvironment (TME)^[2]. Although components of the TME have been shown to regulate angiogenesis^[3] and promote malignant progression and metastasis^[4], their role in the response to RT and their contribution to radioresistance is less well characterised^[5]. As such, a greater understanding of the TME response could identify predictive biomarkers indicative of RT success or failure.

Predictive biomarkers offer an approach for stratifying patients who will respond favourably to a particular treatment, in turn sparing those for whom the modality may be less effective. While radiotherapy is intrinsically a precision treatment, directed to the specific architecture of the patient’s tumour, it has so far lacked a personalised approach, taking into consideration patient-specific genomic alterations or TME composition, factors that could predict the outcome of radiotherapy^[6]^[7].

2. Radiation Response in the Tumour Microenvironment

RT can be a cure for many; however, for some patients, the treatment fails or resistance occurs. Though ionizing radiation can induce DNA damage in tumour cells, a potential barrier to the success of RT may be its effects on the other components of the local TME, including the vasculature, stroma and the immune infiltrate (Figure 1). These components can influence tumour progression and response to treatment. Understanding how they are influenced by RT may be critical in predicting disease outcomes.

Figure 1. The effect of radiation on the TME. Schematic showing the role of ionizing radiation on components of the TME and predictive biomarkers of radiation response. DAMPs, damage-associated molecular patterns; EC, endothelial cell; ECM, extracellular matrix; ICD, immunogenic cell death; MHC, major histocompatibility complex; PD-1, programmed cell death protein 1; PD-L1, programmed death ligand-1; RT, radiotherapy; TAM, tumour-associated macrophage; TCR, T-cell receptor; TGFβ, transforming growth factor beta; TME, tumour microenvironment.

2.1 Tumour Immune Environment

Immune evasion, the process by which tumour cells can avoid immune recognition and destruction, has become one of the hallmarks of cancer^[8]. Subsequently, more recent therapeutic developments have focused on shifting the TME from an immunosuppressive environment to an immune-activated one through the use of immunotherapeutics: treatments that can effectively remove the brakes on immune signals mounting an anti-tumour response. RT has been shown to have contradictory immunomodulatory effects, influencing both proinflammatory and immunosuppressive responses, which likely influence response to treatment^[5]. The inflammatory milieu of the TME, or the tumour immune microenvironment (TIME), is composed of T cells, natural killer (NK) cells, dendritic cells (DCs) and tumour-infiltrating myeloid cells (TIMs) including tumour-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs) and dendritic cells (DCs), all of which are recruited into the TME through altered chemokine and cytokine signalling^[9]. The extent and relative proportion of immune infiltration can also influence the response to treatment and progression. Tumours can be broadly separated into two categories based on their TIME: those that are immune “hot”, being infiltrated with T lymphocytes; and those that are immune “cold”, with poor infiltration^[10]. In immune “hot” tumours, regulatory T cells (Tregs) and TAMs cooperate to support the immunosuppressive TME and may be more susceptible to the immunomodulatory effects of radiotherapy^[11]. Furthermore, these immune-inflamed tumours, including non-small cell lung cancer and melanoma, are more likely to respond favourably to immune checkpoint inhibitors in comparison to immune “cold” tumours, including pancreatic and prostate tumours^[12]. Lack of tumour antigens, defects in antigen presentation and poor T-cell homing to the TME by the stroma may all contribute to a “cold” tumour immune phenotype; mechanisms to modulate immune infiltration and turn these tumours “hot” could improve response to therapy^[12]^[13]^[14].

2.2 Cancer-Associated Fibroblasts

The stromal compartment of the TME plays an integral role in the response to treatment, including RT (Figure 1). Radiotherapy-induced tissue fibrosis is a late side effect where myofibroblast transformation leads to the excess production of collagen and deposition of components of the extracellular matrix (ECM)^[15]. RT can also lead to the release of the pleotropic cytokine transforming growth factor beta (TGFβ), which can modulate fibroblast phenotype and function^[16]. Fibroblasts recruited into the TME are transformed into cancer-associated fibroblasts (CAFs), where they play a role in regulating the extracellular matrix^[17]. Furthermore, CAFs are responsible for the secretion of a number of cytokines (including interleukin 6 (IL6) and IL8), chemokines (including C-X-C motif ligand 12 (CXCL12)) and growth factors (including TGF-β and platelet-derived growth factor (PDGF)) that can influence immune cell fate and tumour progression, often contributing to the immunosuppressive TIME^[18]. However, the effects of RT on the stromal compartment of the TME including CAFs are less well understood and they appear to have contradictory roles, contributing to both tumour growth and suppression^[19].

2.3 Tumour Vasculature

The integrity of the tumour vasculature differs significantly from that of physiologically normal vessels, characterised by abnormal recruitment of pericytes, leading to increased tortuosity and porosity. This, in part, contributes to treatment failure through poor drug penetration into the TME, establishing local hypoxia gradients and increasing the yield of reactive oxygen species^[20]. The effect of RT on the tumour vasculature has been well studied, with tumour blood vessels and their endothelial cells proven to exhibit increased sensitivity to radiation, a response likely dependent on total radiation dose and fractionation schedule^[5]^[21]^[22].

3. Predictive Biomarkers of Radiation Response

Precision medicine based on common tumour-specific alterations, emerging from high-throughput molecular profiling, has become a reality in recent years. This approach underpins the discovery of clinically validated prognostic and/or predictive biomarkers, allowing for stratification of patients based either on those most likely to derive benefit or have treatment-related harm limited. This strategy gained significant momentum in the chemotherapy field with the development of various commercially produced kits such as Prosigna (NanoString Technologies, Inc., Seattle, USA) and MammaPrint (Agendia, Amsterdam, The Netherlands), designed to aid clinical decision-making ^[23]^[24]. However, equivalence in radiotherapy has not yet been achieved due to the variability in radiation response, an effect attributed to tumour heterogeneity. Heterogeneity is an umbrella term used to describe both intra- and intertumour variability at the morphological, physiological and more recently, genetic levels. Divergence of these features exerts a profound influence on localised factors such as vascular integrity, tumour oxygenation and immune infiltrate, ultimately influencing treatment outcome (detailed in^[5]^[11]^[21]). In an effort to address the issue of heterogeneity, research efforts have shifted from focusing on macroscopic phenotypic or environmental variation to the identification of commonality at the molecular level. Table 1 provides an outline of biomarkers for radiotherapy response in a number of tumour types (summarised in Figure 1); these are discussed further in the sections below.

Table 1. Biomarkers of radiotherapy response.

	Year	Cancer Type	Biomarker	Results	Ref
Gene signatures	2012	NCI-60 human tumor cell lines screen	A 31-gene signature developed from meta-analysis of microarray data correlated with clonogenic assay data to identify radiosensitive or radioresistant cells	Genes involved in cell cycle progression (CCNA2, CDK6, CCND1) and DNA damage repair were associated with increased radiosensitivity	^[25]
	2014	Breast cancer	A 7-gene signature applied to the Danish Breast Cancer Cooperative Group (DBCG82bc) cohort to stratify patients into either high-risk locoregional recurrence (LRR) or low-risk LRR	Identified that post-mastectomy RT would benefit only those identified as high risk, providing no benefit to low-risk patients	^[26]
	2015	Breast cancer	Radiation sensitivity gene signature developed from correlating radiation sensitivity (SF2) of a panel of breast cancer models against gene expression changes	Gene signature significantly predicted loco-regional recurrence; beating all clinicopathologic features used in clinical practice	^[27]
	2016	Prostate cancer	A 24-gene signature applied to prostate cancer patients who had undergone radical prostatectomy to identify those most likely to benefit from postoperative radiotherapy	Retrospective analysis identified that those patients with a high PROTOS (post-operative radiation therapy outcomes score), indicative of radiation sensitive tumours, were less likely to develop metastasis at 10 years post-RT. In the low PROTOS score group, radiotherapy proved detrimental	^[28]
	2020	HNSCC	A 12-gene signature	Classified patients with a higher radiosensitivity for whom RT would be beneficial and could predict overall survival.	^[29]
DNA-damage response	2010	Breast cancer	Gene expression signature associated with DDR, correlated against publicly available breast cancer microarray data	DDR-associated genes induced by radiation correlated positively with those who responded favourably to radiation treatment	^[30]
DNA-damage response	2014	Breast cancer	Radiation-induced 30-gene DDR signature	Gene signature was capable of discriminating between breast cancer patients likely to achieve a pathological complete response (pCR) to neoadjuvant chemotherapy and poor-responding patients	^[31]
Hypoxia	2013	Laryngeal cancer	A 26-hypoxia gene signature	Could predict those patients receiving RT for whom hypoxia-modifying ARCON (accelerated radiotherapy with carbogen and nicotinamide) therapy would be of benefit	^[32]
Hypoxia	2012	HNSCC	A 15-gene hypoxia signature	Classified patients who would benefit from combining RT with hypoxia modification (nimorazole)	^[33]
Liquid biopsies	2011	Prostate cancer	Altered miRNA expression: developed through screening of miRNAs in prostate cancer cells (LNCaP) in response to RT	Suppressed miR-221 expression linked with increased radiation sensitivity: data subsequently correlated in clinical datasets where low serum levels of miR-221 are indicative of low-risk prostate cancer	^[34]
Liquid biopsies	2018	Nonmetastatic rectal cancer and head and neck cancers	miRNA expression rations: prediction classifier	The expressions of three miRNAs—miR-374a-5p, miR-342-5p and miR-519d-3p—were significantly different between responsive and poor-responsive RT groups. miRNA classifier successfully predicted radiotherapy outcomes	^[35]
Immune signature	2018	Breast cancer	Combined radiation sensitivity (RS) gene signature with an antigen-presentation (AP) immune signature	Both RS and AP signatures capable of predicting increased disease specific survival (DSS) in patients identified with either radio-sensitive or immune-effective tumours	^[36]

4. Conclusions and Future Perspectives

RT is the treatment of choice for a number of cancer, designed to target and kill tumour cells; however, it triggers a myriad of effects on other components of the TME, including the vasculature, stroma and the immune compartment^[5]. The immunomodulatory effects of RT are complex, with reported changes to the proportions and functionality of T cells and antigen-presenting dendritic cells, and effects on TAM polarisation within the TME. This effect is further complicated by clinical observations of an increase in the abscopal effect reported in patients receiving RT in combination with immunotherapeutics. RT has also been shown to affect tumour vascular architecture, inducing tissue fibrosis. It is important to note that the majority of responses to RT in the TME reported above are in the context of conventional X-ray or photon radiation therapy. Recent advances in the clinical delivery of RT, including high-energy proton beam therapy and heavy ion therapy, have the improvement of delivering more dose in the Bragg peak with a lower dependence on tissue oxygenation and improved biological effectiveness^[37]. While these newer treatment modalities are likely to have biological effects on the components of the TME outlined in this review, their response has been less well characterised^[38]^[39]. Therefore, it is of critical importance to take into consideration the role of the TME when considering radiobiological responses and disease recurrence. As RT techniques have evolved over the last two decades, so too have their physical precision, aided by improved imaging guidance and technological advancements. However, genomic precision has lagged, as most RT treatment planning is designed around the tumour and local tissue architecture, with the aim to deliver the maximum dose to the tumour while sparing healthy tissue. However, as highlighted above, genomic signatures could allow for a greater prediction of those patients for whom RT would be of benefit as a single therapy or in combination with radiation sensitizers or hypoxia modifiers^[6]. Yet, of critical importance, these findings further stress the necessity for a precision medicine approach, in that not only do patients with radioresistant tumours fail to experience radiotherapy benefit, but that treatment is actually detrimental both in terms of DSS and toxicities associated with radiation-induced late effects^[36]. Taking a more “personalised” approach to RT could ensure patients receive the most benefit from their treatment.

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