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Byrne, N. Radiation Responses in Tumour Microenvironment. Encyclopedia. Available online: (accessed on 15 April 2024).
Byrne N. Radiation Responses in Tumour Microenvironment. Encyclopedia. Available at: Accessed April 15, 2024.
Byrne, Niall. "Radiation Responses in Tumour Microenvironment" Encyclopedia, (accessed April 15, 2024).
Byrne, N. (2021, March 02). Radiation Responses in Tumour Microenvironment. In Encyclopedia.
Byrne, Niall. "Radiation Responses in Tumour Microenvironment." Encyclopedia. Web. 02 March, 2021.
Radiation Responses in Tumour Microenvironment

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


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



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



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



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




A 12-gene signature

Classified patients with a higher radiosensitivity for whom RT would be beneficial and could predict overall survival.


DNA-damage response


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



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




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




A 15-gene hypoxia signature

Classified patients who would benefit from combining RT with hypoxia modification (nimorazole)


Liquid biopsies


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



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


Immune signature


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


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.


  1. Jacques Bernier; Eric J. Hall; Amato Giaccia; Radiation oncology: a century of achievements. Nature Reviews Cancer 2004, 4, 737-747, 10.1038/nrc1451.
  2. Mary Helen Barcellos-Hoff; Catherine Park; Eric G. Wright; Radiation and the microenvironment – tumorigenesis and therapy. Nature Reviews Cancer 2005, 5, 867-875, 10.1038/nrc1735.
  3. Michele De Palma; Daniela Biziato; Tatiana V. Petrova; Microenvironmental regulation of tumour angiogenesis. Nature Reviews Cancer 2017, 17, 457-474, 10.1038/nrc.2017.51.
  4. Daniela F Quail; Johanna A Joyce; Microenvironmental regulation of tumor progression and metastasis. Nature Medicine 2013, 19, 1423-1437, 10.1038/nm.3394.
  5. Holly E. Barker; James T. E. Paget; Aadil A. Khan; Kevin J. Harrington; The tumour microenvironment after radiotherapy: mechanisms of resistance and recurrence. Nature Reviews Cancer 2015, 15, 409-425, 10.1038/nrc3958.
  6. Scott V Bratman; Michael F Milosevic; Fei-Fei Liu; Benjamin Haibe-Kains; Genomic biomarkers for precision radiation medicine.. The Lancet Oncology 2017, 18, e238, 10.1016/S1470-2045(17)30263-2.
  7. Jacob G Scott; Anders Berglund; Michael J Schell; Ivaylo Mihaylov; William J Fulp; Binglin Yue; Eric Welsh; Jimmy J Caudell; Kamran Ahmed; Tobin S Strom; et al.Eric MellonPuja VenkatPeter JohnstoneJohn FoekensJae LeeEduardo MorosWilliam S DaltonSteven A EschrichHoward McLeodLouis B HarrisonJavier F Torres-Roca A genome-based model for adjusting radiotherapy dose (GARD): a retrospective, cohort-based study. The Lancet Oncology 2017, 18, 202-211, 10.1016/s1470-2045(16)30648-9.
  8. Douglas Hanahan; Robert A. Weinberg; Hallmarks of Cancer: The Next Generation. Cell 2011, 144, 646-674, 10.1016/j.cell.2011.02.013.
  9. Mikhail Binnewies; Edward W. Roberts; Kelly Kersten; Vincent Chan; Douglas F. Fearon; Miriam Merad; Lisa M. Coussens; Dmitry I. Gabrilovich; Suzanne Ostrand-Rosenberg; Catherine C. Hedrick; et al.Robert H. VonderheideMikael J. PittetRakesh K. JainWeiping ZouT. Kevin HowcroftElisa C. WoodhouseRobert A. WeinbergMatthew F. Krummel Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Medicine 2018, 24, 541-550, 10.1038/s41591-018-0014-x.
  10. Thomas F. Gajewski; The Next Hurdle in Cancer Immunotherapy: Overcoming the Non–T-Cell–Inflamed Tumor Microenvironment. Seminars in Oncology 2015, 42, 663-671, 10.1053/j.seminoncol.2015.05.011.
  11. Wolf Herman Fridman; Franck Pagès; Catherine Sautès-Fridman; Jérôme Galon; The immune contexture in human tumours: impact on clinical outcome. Nature Reviews Cancer 2012, 12, 298-306, 10.1038/nrc3245.
  12. Paola Bonaventura; Tala Shekarian; Vincent Alcazer; Jenny Valladeau-Guilemond; Sandrine Valsesia-Wittmann; Sebastian Amigorena; Christophe Caux; Stéphane Depil; Cold Tumors: A Therapeutic Challenge for Immunotherapy. Frontiers in Immunology 2019, 10, 168, 10.3389/fimmu.2019.00168.
  13. Qianqian Duan; Hualing Zhang; Junnian Zheng; Lianjun Zhang; Turning Cold into Hot: Firing up the Tumor Microenvironment. Trends in Cancer 2020, 6, 605-618, 10.1016/j.trecan.2020.02.022.
  14. Jérôme Galon; Daniela Bruni; Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nature Reviews Drug Discovery 2019, 18, 197-218, 10.1038/s41573-018-0007-y.
  15. Jeffrey M. Straub; Jacob New; Chase D. Hamilton; Chris Lominska; Yelizaveta Shnayder; Sufi M. Thomas; Radiation-induced fibrosis: mechanisms and implications for therapy. Journal of Cancer Research and Clinical Oncology 2015, 141, 1985-1994, 10.1007/s00432-015-1974-6.
  16. Horatiu Dancea; Role of Radiation-induced TGF-beta Signaling in Cancer Therapy. Molecular and Cellular Pharmacology 2009, 1, 44-56, 10.4255/mcpharmacol.09.06.
  17. Erik Sahai; Igor Astsaturov; Edna Cukierman; David G. DeNardo; Mikala Egeblad; Ronald M. Evans; Douglas Fearon; Florian R. Greten; Sunil R. Hingorani; Tony Hunter; et al.Richard O. HynesRakesh K. JainTobias JanowitzClaus JorgensenAlec C. KimmelmanMikhail G. KoloninRobert G. MakiR. Scott PowersEllen PuréDaniel C. RamirezRuth Scherz-ShouvalMara H. ShermanSheila StewartThea D. TlstyDavid A. TuvesonFiona M. WattValerie WeaverAshani T. WeeraratnaZena Werb A framework for advancing our understanding of cancer-associated fibroblasts. Nature Reviews Cancer 2020, 20, 174-186, 10.1038/s41568-019-0238-1.
  18. Debolina Ganguly; Raghav Chandra; John Karalis; Martha Teke; Todd Aguilera; Ravikanth Maddipati; Megan B. Wachsmann; Dario Ghersi; Giulia Siravegna; Herbert Iii; et al.Rolf BrekkenDavid T. TingMatteo Ligorio Cancer-Associated Fibroblasts: Versatile Players in the Tumor Microenvironment. Cancers 2020, 12, 2652, 10.3390/cancers12092652.
  19. Zhanhuai Wang; Yang Tang; Yinuo Tan; Qichun Wei; Wei Yu; Cancer-associated fibroblasts in radiotherapy: challenges and new opportunities. Cell Communication and Signaling 2019, 17, 47, 10.1186/s12964-019-0362-2.
  20. Madyson Colton; Eleanor J. Cheadle; Jamie Honeychurch; Tim M. Illidge; Reprogramming the tumour microenvironment by radiotherapy: implications for radiotherapy and immunotherapy combinations. Radiation Oncology 2020, 15, 1-11, 10.1186/s13014-020-01678-1.
  21. J. Martin Brown; Radiation Damage to Tumor Vasculature Initiates a Program That Promotes Tumor Recurrences. International Journal of Radiation Oncology*Biology*Physics 2020, 108, 734-744, 10.1016/j.ijrobp.2020.05.028.
  22. Katherine D. Castle; David G. Kirsch; Establishing the Impact of Vascular Damage on Tumor Response to High-Dose Radiation Therapy. Cancer Research 2019, 79, 5685-5692, 10.1158/0008-5472.can-19-1323.
  23. Maj-Britt Jensen; Anne-Vibeke Lænkholm; Eva Balslev; Wesley Buckingham; Sean Ferree; Vesna Glavicic; Jeanette Dupont Jensen; Ann Søegaard Knoop; Henning T. Mouridsen; Dorte Nielsen; et al.Torsten O. NielsenBent Ejlertsen The Prosigna 50-gene profile and responsiveness to adjuvant anthracycline-based chemotherapy in high-risk breast cancer patients. npj Breast Cancer 2020, 6, 1-9, 10.1038/s41523-020-0148-0.
  24. Fatima Cardoso; Laura J. Van’T Veer; Jan Bogaerts; Leen L. Slaets; Giuseppe Viale; Suzette Delaloge; Jean Yves Ves J.Y. Pierga; Etienne Brain; Sylvain S. Causeret; Mauro Delorenzi; et al.Annuska M. GlasVassilis GolfinopoulosTheodora T. GouliotiSusan S. KnoxErika E. MatosBart MeulemansPeter P.A. NeijenhuisUlrike NitzRodolfo PassalacquaPeter Marcus P. RavdinIsabel Teresa RubioMahasti SaghatchianTineke T.J. SmildeChristos SotiriouLisette L. Stork-SlootsCarolyn StraehleGeraldine ThomasAlastair M. ThompsonJacobus J.M. Van Der HoevenPeter VuylstekeRené R. BernardsKonstantinos K. TryfonidisEmiel Th RutgersMartine Piccart-Gebhart 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. New England Journal of Medicine 2016, 375, 717-729, 10.1056/nejmoa1602253.
  25. Han Sang Kim; Sang Cheol Kim; Sun Jeong Kim; Chan Hee Park; Hei-Cheul Jeung; Yong Bae Kim; Joong Bae Ahn; Hyun Cheol Chung; Sun Young Rha; Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells. BMC Genomics 2012, 13, 348-348, 10.1186/1471-2164-13-348.
  26. Trine Tramm; Hayat Mohammed; Simen Myhre; Marianne Kyndi; Jan Alsner; Anne-Lise Børresen-Dale; Therese Sørlie; Arnoldo Frigessi; Jens Overgaard; Development and Validation of a Gene Profile Predicting Benefit of Postmastectomy Radiotherapy in Patients with High-Risk Breast Cancer: A Study of Gene Expression in the DBCG82bc Cohort. Clinical Cancer Research 2014, 20, 5272-5280, 10.1158/1078-0432.ccr-14-0458.
  27. Corey Speers; Shuang Zhao; Meilan Liu; Harry Bartelink; Lori J. Pierce; Felix Y. Feng; Development and Validation of a Novel Radiosensitivity Signature in Human Breast Cancer. Clinical Cancer Research 2015, 21, 3667-3677, 10.1158/1078-0432.ccr-14-2898.
  28. Shuang G Zhao; S Laura Chang; Daniel E Spratt; Nicholas Erho; Menggang Yu; Hussam Al-Deen Ashab; Mohammed Alshalalfa; Corey Speers; Scott A Tomlins; Elai Davicioni; et al.Adam P DickerPeter R CarrollMatthew R CooperbergStephen J FreedlandR Jeffrey KarnesAshley E RossEdward M SchaefferRobert B DenPaul L NguyenFelix Y Feng Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. The Lancet Oncology 2016, 17, 1612-1620, 10.1016/s1470-2045(16)30491-0.
  29. Jie Liu; Mengmeng Han; Zhenyu Yue; Chao Dong; Pengbo Wen; Guoping Zhao; Lijun Wu; Junfeng Xia; Yannan Bin; Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data. Frontiers in Genetics 2020, 11, 960, 10.3389/fgene.2020.00960.
  30. Brian D. Piening; Pei Wang; Aravind Subramanian; Amanda G. Paulovich; A Radiation-Derived Gene Expression Signature Predicts Clinical Outcome for Breast Cancer Patients. Radiation Research 2009, 171, 141-154, 10.1667/rr1223.1.
  31. Daniel S. Oh; Maggie C. U. Cheang; Cheng Fan; Charles M. Perou; Radiation-induced gene signature predicts pathologic complete response to neoadjuvant chemotherapy in breast cancer patients.. Radiation Research 2014, 181, 193-207, 10.1667/RR13485.1.
  32. Amanda Eustace; Navin Mani; Paul N. Span; Joely J. Irlam; Janet Taylor; Guy N.J. Betts; Helen Denley; Crispin J. Miller; Jarrod J. Homer; Ana M. Rojas; et al.Peter J. HoskinFrancesca M. BuffaAdrian L. HarrisJohannes H.A.M. KaandersCatharine M.L. West A 26-Gene Hypoxia Signature Predicts Benefit from Hypoxia-Modifying Therapy in Laryngeal Cancer but Not Bladder Cancer. Clinical Cancer Research 2013, 19, 4879-4888, 10.1158/1078-0432.ccr-13-0542.
  33. Kasper Toustrup; Brita Singers Sørensen; Pernille Lassen; Carsten Wiuf; Jan Alsner; Jens Overgaard; Gene expression classifier predicts for hypoxic modification of radiotherapy with nimorazole in squamous cell carcinomas of the head and neck. Radiotherapy and Oncology 2012, 102, 122-129, 10.1016/j.radonc.2011.09.010.
  34. Baoqing Li; Xu-Bao Shi; Dattatreyudu Nori; Clifford K.S. Chao; Allen M. Chen; Richard Valicenti; Ralph De Vere White; Down-regulation of microRNA 106b is involved in p21-mediated cell cycle arrest in response to radiation in prostate cancer cells. The Prostate 2010, 71, 567-574, 10.1002/pros.21272.
  35. An-Lun Li; Tao-Sang Chung; Yao-Ning Chan; Chien-Lung Chen; Shih-Chieh Lin; Yun-Ru Chiang; Chen-Huan Lin; Chi-Ching Chen; Nianhan Ma; microRNA expression pattern as an ancillary prognostic signature for radiotherapy. Journal of Translational Medicine 2018, 16, 341, 10.1186/s12967-018-1711-4.
  36. Yi Cui; Bailiang Li; Erqi Liu Pollom; Kathleen C. Horst; Ruijiang Li; Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer. Clinical Cancer Research 2018, 24, 4754-4762, 10.1158/1078-0432.ccr-18-0825.
  37. Karen Joy Kirkby; Norman Francis Kirkby; Neil Gunn Burnet; Hywel Owen; Ranald Iain Mackay; Adrian Crellin; Stuart Green; Heavy charged particle beam therapy and related new radiotherapy technologies: The clinical potential, physics and technical developments required to deliver benefit for patients with cancer. The British Journal of Radiology 2020, 93, 20200247, 10.1259/bjr.20200247.
  38. M Lupu-Plesu; A Claren; S Martial; P-D N'diaye; K Lebrigand; N Pons; D Ambrosetti; I Peyrottes; J Feuillade; J Hérault; et al.M DufiesJ DoyenG Pagès Effects of proton versus photon irradiation on (lymph)angiogenic, inflammatory, proliferative and anti-tumor immune responses in head and neck squamous cell carcinoma. Oncogenesis 2017, 6, e354-e354, 10.1038/oncsis.2017.56.
  39. Marco Durante; Silvia Formenti; Harnessing radiation to improve immunotherapy: better with particles?. The British Journal of Radiology 2020, 93, 20190224, 10.1259/bjr.20190224.
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