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TIME, DOSE AND FRACTIONATION: ACCOUNTING FOR HYPOXIA IN THE SEARCH FOR OPTIMAL RADIOTHERAPY TREATMENT PARAMETERS

Emely Kjellsson Lindblom

Time, dose and fractionation: accounting for hypoxia in the search for optimal radiotherapy treatment parameters

Emely Kjellsson Lindblom ©Emely Kjellsson Lindblom, Stockholm University 2017

ISBN print 978-91-7797-031-6 ISBN PDF 978-91-7797-032-3

Printed in Sweden by Universitetsservice US-AB, Stockholm 2017 Distributor: Department of Physics Abstract

The search for the optimal choice of treatment time, dose and fractionation regimen is one of the major challenges in radiation therapy. Several aspects of the radiation response of tumours and normal tissues give different indica- tions of how the parameters defining a fractionation schedule should be altered relative to each other which often results in contradictory conclusions. For example, the increased sensitivity to fractionation in late-reacting as opposed to early-reacting tissues indicates that a large number of fractions is beneficial, while the issue of accelerated repopulation of tumour cells starting at about three weeks into a radiotherapy treatment would suggest as short overall treatment time as possible. Another tumour-to-normal tissue differential relevant to the sensitivity as well as the fractionation and overall treatment time is the issue of tumour hypoxia and reoxygenation. The tumour oxygenation is one of the most influential factors impacting on the outcome of many types of treatment modalities. Hypoxic cells are up to three times as resistant to radiation as well-oxygenated cells, presenting a significant obstacle to overcome in radiotherapy as solid tumours often contain hypoxic areas as a result of their poorly functioning vasculature. Furthermore, the oxygenation is highly dynamic, with changes being observed both from fraction to fraction and over a time period of weeks as a result of fast and slow reoxygenation of acute and chronic hypoxia. With an increasing number of patients treated with hypofractionated stereotactic body radiotherapy (SBRT), the clinical implications of a substantially reduced number of fractions and hence also treatment time thus have to be evaluated with respect to the oxygenation status of the tumour. One of the most promising tools available for the type of study aiming at determining the optimal radiotherapy approach with respect to fractionation is radiobiological modelling. With clinically validated in vitro-derived tissue- specific radiobiological parameters and well-established survival models, in silico modelling offers a wide range of opportunities to test various hypotheses with respect to time, dose, fractionation and details of the tumour microenvironment. Any type of radiobiological modelling study intended to provide a realistic representation of a clinical tumour should therefore take into account details of both the spatial and temporal tumour oxygenation. This thesis presents the results of three-dimensional radiobiological modelling of the response of tumours with heterogeneous oxygenation to various fractionation schemes, and oxygenation levels and dynamics using different survival models. The results of this work indicate that hypoxia and its dynamics play a major role in the outcome of radiotherapy, and that neglecting the oxygenation status of tumours treated with e.g. SBRT may compromise the treatment outcome substantially. Furthermore, the possibilities offered by incorporating modelling into the clinical routine are explored and demonstrated by the development of a new calibration function for converting the uptake of the hypoxia-PET tracer 18F-HX4 to oxygen partial pressure, and applying it for calculations of the doses needed to overcome hypoxia-induced radiation resistance. By hence demonstrating how the clinical impact of hypoxia on dose prescription and the choice of fractionation schedule can be investigated, this project will hopefully advance the evolution towards routinely incorporating functional imaging of hypoxia into treatment planning. This is ultimately expected to result in increased levels of local control with more patients being cured from their cancer. Sammanfattning

Idag drabbas var tredje person av cancer, och hälften av dem får strålbehand- ling. En strålbehandling ges vanligtvis vid ett flertal behandlingstillfällen, där den totala stråldosen delas upp i ett antal så kallade fraktioner. Beroende på antalet fraktioner kan behandlingen pågå under allt ifrån en dag till flera veckor. Att identifiera den kombination av den totala stråldosen, antalet fraktioner, och den totala behandlingstiden som maximerar sannolikheten att oskadliggö- ra tumören samtidigt som påverkan på den friska vävnaden minimeras är en av de stora utmaningarna inom strålterapi. Tumörer har ofta syrebrist, vilket är problematiskt eftersom celler med syrebrist kan vara upp till tre gånger så motståndskraftiga mot joniserande strål- ning som väl syresatta celler. Vidare är syrefördelningen i tumörer föränderlig över tid, och kan variera såväl från dag till dag som över en period om flera veckor. Detta innebär att tumörens syresättning kan ha stor betydelse för vilken behandlingsstrategi som är mest fördelaktig. Idag behandlas fler och fler pati- enter med så kallad hypofraktionerad stereotaktisk strålbehandling bestående av endast ett fåtal fraktioner med hög dos. Detta kan tilltala både patienten och kliniken, då färre behandlingstillfällen krävs. En nyckelfråga är dock om en sådan behandling riskerar att äventyra sannolikheten att oskadliggöra tumö- ren, särskilt för tumörer med syrebrist, samt om det ur detta avseende finns ett optimalt antal fraktioner för att maximera utfallet av behandlingen. I den här avhandlingen används matematiska modeller som genom väv- nadsspecifika parametrar beskriver sambandet mellan fysikaliska storheter och den biologiska effekten i syfte att undersöka kombinationer av den totala strål- dosen, antalet fraktioner, och den totala behandlingstiden som kan maximera sannolikheten att oskadliggöra tumören. Resultaten påvisar vikten av att inklu- dera information om tumörens syresättning vid valet av behandlingsstrategi, samt att syresättningens utbredning och dynamik spelar en stor roll för utfallet av behandlingen. Vidare undersöks hur radiobiologiska modeller kan använ- das för att identifiera områden med syrebrist i tumörer baserat på bildtagning, samt för att anpassa såväl stråldosen som andra nyckelaspekter av behandlingen. Detta kommer förhoppningsvis att bidra till en utveckling mot att tumörers syresättning rutinmässigt inkluderas i valet av behandlingsstrategi, vilket slut- ligen förväntas öka antalet botade cancerpatienter.

List of papers

PAPER I: Survival and tumour control probability in tumours with heterogeneous oxygenation: a comparison between the linear- quadratic and the universal survival curve models for high doses E. Lindblom, A. Dasu, I. Lax, I. Toma-Dasu, Acta Oncologica, 53(8): 1035-40 (2014).

PAPER II: Accounting for two forms of hypoxia for predicting tumour control probability in radiotherapy – an in silico study E. Lindblom, A. Dasu, I. Toma-Dasu, Advances in Experimen- tal Medicine and Biology (under review), (2017).

PAPER III: Treatment fractionation for stereotactic radiotherapy of lung tumours: a modelling study of the inﬂuence of chronic and acute hypoxia on tumour control probability E. Lindblom, L. Antonovic, A. Dasu, I. Lax, P. Wersäll, I. Toma- Dasu, Radiation Oncology, 9: Article ID 149 (2014).

PAPER IV: Optimal fractionation in radiotherapy for non-small cell lung cancer – a modelling approach E. Lindblom, A. Dasu, I. Toma-Dasu, Acta Oncologica, 54(9): 1592-8 (2015).

PAPER V: Deﬁning the hypoxic target volume based on positron emission tomography for image guided radiotherapy – the in- ﬂuence of the choice of the reference region and conversion function E. Lindblom, A. Dasu, J. Uhrdin, A. Even, W. van Elmpt, P. Lambin, P. Wersäll, I. Toma-Dasu, Acta Oncologica, 56(6): 819- 25 (2017). PAPER VI: Non-linear conversion of HX4 uptake for automatic segmentation of hypoxic volumes and dose prescription A. Ureba, E. Lindblom, A. Dasu, J. Uhrdin, A. Even, W. van Elmpt, P. Lambin, P. Wersäll, I. Toma-Dasu, Acta Oncologica (accepted for publication), (2017)

Publications relevant to, but not included in the thesis

PAPER VII: Clinical oxygen enhancement ratio of tumors in carbon ion radiotherapy: the inﬂuence of local oxygenation changes L. Antonovic, E. Lindblom, A. Dasu, N. Bassler, Y. Furusawa, I. Toma-Dasu, Journal of Radiation Research, 55(5): 902-11 (2014).

PAPER VIII: High brachytherapy doses can counteract hypoxia in cervical cancer – a modelling study E. Lindblom, A. Dasu, C. Beskow, I. Toma-Dasu, Physics in Medicine and Biology, 62(2): 560-572 (2017). Author’s contribution

PAPER I: I took part in the design of the study, developed the existing code to perform the necessary calculations and simulations, performed all simulations producing the results and took part in evaluating and selecting which results to be displayed. I also did the main writing of the manuscript.

PAPER II: I designed the study together with the co-authors, developed the existing code to perform the necessary calculations and simulations, performed all simulations and hence produced and selected the results to be displayed. I took part in reviewing the manuscript.

PAPER III: I performed all simulations producing the results and took part in evaluating and selecting which results to be displayed. I also did the main writing of the manuscript.

PAPER IV: I designed the study together with the co-authors, developed the existing code to perform the necessary calculations and simulations, performed all simulations producing the results and took part in evaluating and selecting which results to be displayed. I also did the main writing of the manuscript.

PAPER V: I designed the study together with the co-authors, developed the methodology, produced the results and took part in evaluating and selecting which results to be displayed. I also did the main writing of the manuscript.

PAPER VI: I designed the study together with the co-authors, took part in developing the methodology, produced the results and took part in evaluating and selecting which results to be displayed. I also did the main writing of the manuscript.

Outline of the thesis

This thesis, introducing the background to and summarising the work presented in papers I-VI, focuses on investigating the impact of hypoxia on the outcome of fractionated radiotherapy. The ﬁrst part of the thesis describes the results of modelling the response of in silico tumours with heterogeneous and dynamic tumour oxygenation, while the second part is concerned with the clinical aspects of assessing tumour hypoxia and incorporating it into the treatment planning process for an improved outcome of radiotherapy.

As this thesis includes some of the work presented in my licentiate thesis from December 2014, part of the text constituting this thesis was originally included in my licentiate thesis. The following chapters, sections and paragraphs have been fully or partially reproduced:

The abstract, except the last paragraph which has been adapted to include the work following the licentiate.

The introduction has been re-processed to better introduce the whole thesis, including the work following the licentiate.

In chapter 2, sections 2.1 and 2.2 were originally included in the licentiate thesis, as well as the three ﬁrst paragraphs of section 2.3, and paragraphs 1-2 on page 24.

In chapter 3, paragraph 1 has been adapted from the licentiate thesis. Parts of sections 3.1 and 3.2 were originally included in the licentiate thesis while section 3.3 has been adapted from the licentiate thesis. Part of section 3.4 was also originally included in the licentiate thesis. Figure 3.2 has been adopted and developed from the licentiate thesis.

Finally, the ﬁrst two paragraphs of chapter 5 were also originally included in my licentiate thesis.

Contents

Abstract i

Sammanfattning iii

List of papers v

Author’s contribution vii

Outline of the thesis ix

Abbreviations xiii

1 Introduction 15

2 Tumour hypoxia: radiobiological aspects and theoretical modelling 17 2.1 The oxygen effect ...... 17 2.2 The origin and mechanisms of tumour hypoxia ...... 18 2.2.1 Chronic hypoxia and slow reoxygenation ...... 19 2.2.2 Acute hypoxia and fast reoxygenation ...... 20 2.3 Modelling tumour oxygenation and its inﬂuence on cell survival 21

3 Time, dose and fractionation: radiobiological aspects and theoretical modelling 27 3.1 Fractionation ...... 27 3.1.1 Sensitivity to fractionation ...... 27 3.1.2 Inter-fraction reoxygenation ...... 28 3.2 Dose per fraction ...... 29 3.3 Overall treatment time ...... 30 3.4 Heterogeneous fractionation patterns ...... 31

4 Clinical aspects and implications of tumour hypoxia 33 4.1 Overview of strategies to overcome hypoxia ...... 34 4.1.1 Sensitising techniques ...... 34 4.1.2 Dose and fractionation techniques ...... 35 4.2 Overview of methods to measure tumour oxygenation ..... 37 4.2.1 Direct and invasive methods ...... 37 4.2.2 Indirect and non-invasive methods ...... 39 4.3 Hypoxia dose painting ...... 41 4.3.1 Deﬁning a hypoxic target volume ...... 42 4.3.2 Dose prescription based on imaging hypoxia ..... 44 4.3.3 Dose prescription based on multi-modal functional imaging...... 46 4.3.4 The importance of clinical validation of radiobiological modelling ...... 47

5 Concluding remarks 49

6 Summary of papers 51

Acknowledgements lv

References lvii Abbreviations

ARCON Accelerated radiotherapy with carbogen and nicotinamide BED Biological effective dose BMDC Bone marrow derived cells BOLD Blood-oxygen-level-dependent BTV Biological target volume CFRT Conventionally fractionated radiotherapy CHART Continuous hyperfractionated accelerated radiotherapy CTV Clinical target volume Cu-ATSM Copper-diacetyl-bis(N4-methylthiosemicarbazone)

D50 The dose corresponding to 50% tumour control probability DCE Dynamic contrast-enhanced DPBC Dose painting by contours DPBN Dose painting by numbers DSB Double-strand break DWI Diffusion-weighted imaging EF5 2-Nitroimidazol-pentaﬂuoropropyl acetamide EPR Electron paramagnetic resonance FAZA Fluoroazomycin-arabinozide FDG Fluorodeoxyglucose FETA Fluoroetanidazole FETNIM Fluoroerythronitroimidazole FLT Fluorothymidine FMISO Fluoromisonidazole GTV Gross target volume HTV Hypoxic target volume HX4 Flortanidazole IRR Inducible repair ratio IVD Inter-vessel distance LET Linear energy transfer LQ Linear quadratic MRI Magnetic resonance imaging NIRS Near-infrared spectroscopy NSCLC Non-small cell lung cancer OER Oxygen enhancement ratio PET Positron emission tomography pO2 Oxygen partial pressure SBRT Stereotactic body radiotherapy SF Surviving fraction

SF2 Surviving fraction at 2 Gy SHMT Single-hit multi-target SOBP Spread-out Bragg peak SUV Standardised uptake value TBR Tumour-to-blood ratio TCP Tumour control probability TMR Tumour-to-muscle ratio USC Universal survival curve VEGF Vascular endothelial growth factor 1. Introduction

For more than a century, ionising radiation has been applied in medicine for the purpose of treating cancer and other lesions. The radiobiological rationale for how to administer the dose in order to maximise the probability to eradi- cate the tumour free of complications has been developed in parallel, resulting in the formulation of the so-called 5 Rs of fractionation (repair, reassortment, repopulation, reoxygenation and radiosensitivity). These constitute the basis of modern photon radiotherapy where, conventionally, a large number of low- dose fractions separated by enough time to allow for complete repair of sub- lethal damages is delivered over a period of several weeks. However, such conventionally fractionated radiotherapy (CFRT) has proven ineffective in treating major cancer groups such as non-small cell lung cancer (NSCLC) and head and neck cancer (Bourhis et al, 2006; Onishi et al, 2004). To improve the outcome of radiotherapy in these patients, the biological effectiveness of the treatment must therefore be increased without increasing the normal tissue toxicity. While the most straightforward strategy is to escalate the radiation dose, this is rarely feasible within the limits of the normal tissue toxicity constraints. To improve the outcome of radiotherapy in these patients, the biological effectiveness of the treatment must therefore instead be increased by other means such as altering the fractionation pattern. For NSCLC, stereotactic body radiotherapy (SBRT) employing few fractions of high doses has resulted in promising results with high levels of local control (Baumann et al, 2009). In head and neck cancer, other fractionation strategies, such as accelerated hyperfractionation (Kaanders et al, 2002), have been pursued to improve local control and reduce normal tissue toxicity. Like many solid tumours, both head and neck- and non-small cell lung cancers often contain hypoxia, one of the greatest obstacles to overcome in radiotherapy as well as in several other treatment modalities. Apart from the increased radioresistance of hypoxic compared to normoxic cells, hypoxia is also associated with a more aggressive phenotype increasing the risk of metastasis (Chan & Giac- cia, 2007). As the positive clinical experience with SBRT in NSCLC is giving conﬁdence in the technique, there has been a trend towards reducing the number of fractions even further provided that the toxicity of the normal tissue is not escalated. Such extreme hypofractionation might however compromise the treatment outcome in hypoxic tumours given the reduced possibility for reoxygention between fractions, and the optimal SBRT treatment approach is therefore yet to be found.

15 While the current practice of radiotherapy with respect to dose and fractionation is mostly empirically derived, one of the potentially most promising tools available for a study aiming at determining the optimal radiotherapy treatment approach with respect to fractionation is radiobiological modelling. With clinically validated in vitro-derived tissue-speciﬁc radiobiological parameters and well-established survival models, in silico modelling offers a wide range of opportunities to test various hypotheses with respect to time, dose, fractionation and details of the tumour microenvironment. Given the complexity and dynamics of tumour hypoxia, a radiobiological modelling approach aiming at producing a realistic representation of a clinical scenario should therefore include details of both the spatial and the temporal tumour oxygenation and its inﬂuence on the treatment outcome. In this thesis, the impact of hypoxia on the choice of advanced radiotherapy treatment regimens is discussed, including the results of three-dimensional radiobiological modelling of the response of tumours with heterogeneous oxygenation to various fractionation schemes, oxygenation levels and dynamics using different survival models. The incorporation of modelling into the clinical routine is also explored, with respect to the pre-treatment assessment of tumour oxygenation as well as the design of the treatment.

16 2. Tumour hypoxia: radiobiological aspects and theoretical modelling

2.1 The oxygen effect

Ionising radiation interacts with matter and deposits dose through ionisations of the target atoms. In biological materials, these interactions lead to damage that may affect the irradiated cells to various extents, and the DNA molecule is considered to be the critical target for ionising radiation. Charged particles, either as constituents of the primary beam or formed as secondary particles, may ionise the DNA molecule directly or interact to form highly reactive free radicals which in turn damage the DNA. These processes are respectively referred to as the direct and indirect mode of action (Hall & Giaccia, 2012). The spatial pattern of damage inflicted by ionising radiation is stochastic. Photons and electrons have low linear energy transfer (LET), defined as the energy transferred per unit length of a radiation track (commonly quoted in keV/μm) (ICRU 62, 1999). For low-LET radiation, the indirect mode of action is dominant and the majority of DNA damage is caused by the free radicals. With increasing LET, ionisations appear more densely along the particle track corresponding to an increase in the direct mode of action. If molecular oxygen is present at the time of or shortly after irradiation, the DNA damage induced by the free radicals may become fixated, leading to an increase in the biological effect. In contrast, damage in cells lacking oxygen will be more readily repaired making those cells more resistant to radiation. This effect can be quantified by the oxygen enhancement ratio (OER), which is the ratio of the doses required in hypoxic to oxic conditions to achieve the same biological effect (Figure 2.1). Depending on the level of hypoxia and the biological end-point, the OER for photons is about three in anoxic conditions. With increasing LET, the fate of an irradiated cell is less dependent on the presence of oxygen as a result of the increasing dominance of the direct mode of action. For high-LET radiation the OER thus approaches unity (Hall & Giaccia, 2012).

17 Figure 2.1: Illustration of OER. Survival curves for cells irradiated in oxic (red) and hypoxic (blue) conditions. The OER is given by the ratio of the doses required in hypoxic to oxic conditions to achieve the same level of biological effect, for example 10% surviving fraction (SF).

In any radiotherapeutic scenario where the OER cannot be expected to be uniformly equal to one, it is quite clear that ignoring the oxygen effect could impact substantially on the success of the treatment. This is of particular importance in the radiation treatment of cancer, as tumours are often hypoxic and thus more radioresistant than normal tissues.

2.2 The origin and mechanisms of tumour hypoxia

Tumours become hypoxic as a result of the particularities of their vasculature. The uncontrolled proliferation of tumour cells, as described by the hallmarks of cancer (Hanahan & Weinberg, 2000), results in a rapid growth of the tumour mass and volume which does not allow the formation of an adequate vascular network. Tumour vessels are typically irregular, unstructured and immature in the sense that they lack the elasticity required for an efficient delivery of oxygen and nutrients. Cells that are consequently not supplied with a sufficient amount of oxygen become hypoxic to various degrees resulting in a modification of their sensitivity to radiation as described by the oxygen effect. In the initial stage of tumour growth, the tumour cells survive through the parasitation of adjacent normal vessels. When the need for oxygen and nutrients exceeds that which can be provided by these vessels, the tumour has

18 to initiate the creation of its own vascular supply. The two major processes through which this is achieved are angiogenesis and vasculogenesis. Angiogenesis is the process in which pre-existing normal vessels are stimulated to form new capillaries to supply the growing tumour. This results from an imbalance in the regulation of promoting and inhibiting the release of angiogenic growth factors in favour of promoting angiogenesis (Hanahan & Folk- man, 1996). Several of the growth factors stimulating angiogenesis such as the vascular endothelial growth factor (VEGF) are furthermore partly driven by hypoxia. In addition to the poor functionality of the vessels, tumours tend to derive their blood supply from the venous side of the normal vasculature which contains blood that is already deprived of oxygen (Vaupel, Kallinowski & Okunieff, 1989). Vasculogenesis is the process in which de novo vessels are formed through the recruitment of circulating bone marrow derived cells (BMDC), independent of the pre-existing normal vasculature. Damaged vasculature of irradiated tumours has been found to be restored by the recruitment of these cells, allowing surviving tumour cells to grow through vasculogenesis. It has furthermore been suggested that it is increased tumour hypoxia, caused by the loss of functional vasculature, that stimulates the tumour influx of BMDC (Kioi et al, 2010). The specific characteristics of the tumour vasculature result in an oxygenation that is insufficient, heterogeneous and dynamic. Two main patterns of hypoxia with different associated temporal behaviours can be identified as chronic and acute hypoxia, although further differentiation of hypoxic subtypes has been proposed (Vaupel & Mayer, 2014). Distinguishing between different kinds of hypoxia is important from many points of view, with respect to predicting and evaluating the response to a treatment schedule as well as choosing optimal treatment parameters.

2.2.1 Chronic hypoxia and slow reoxygenation

Tumour cells located almost up to the diffusion distance of the closest capil- lary will become chronically deprived of oxygen. These cells might be either sensitive or resistant to radiation compared to well-oxygenated cells depending on the severity of the deprivation of oxygen and nutrients. Moderately hypoxic cells with a decreased level of oxygen while still receiving enough nutrients to sustain their viability could be more resistant, while severely hypoxic cells on the verge of necrosis could be expected to be more easily eradicated (Zölzer & Streffer, 2002). Over the course of a conventionally fractionated radiotherapy (CFRT) treatment lasting for several weeks, tumour shrinkage resulting from the eradication of cells is likely to be observed. As the most sensitive cells are effec-

19 tively killed by the radiation and thus stop consuming the diffusing oxygen, chronically hypoxic cells may become reoxygenated by the oxygen that hence becomes available to them. This process, referred to as slow reoxygenation, occurs over a period of several weeks and leads to a radiosensitisation of the hypoxic cells, making them more effectively targeted for the remainder of the treatment (Hall & Giaccia, 2012; Kallman, 1972).

Figure 2.2: Illustration of slow reoxygenation. The colour scale indicates the oxygen partial pressure (pO2) in a cross section through a spherical tumour presenting a large hypoxic core in the beginning of the treatment that becomes smaller due to reoxygenation.

2.2.2 Acute hypoxia and fast reoxygenation A consequence of the bulky, inelastic structure of the tumour vasculature is that the vessels can be temporarily occluded causing a state of acute hypoxia in the nearby cells. This type of instantaneous lack of oxygen exclusively leads to an increased radioresistance, as the suddenly hypoxic cells have not previously been in a constant state of starvation (Zölzer & Streffer, 2002). Similar to the random collapse of tumour vessels leading to acute hypoxia, ﬂuctuations in e.g. the interstitial ﬂuid pressure can result in the re-opening of a previously collapsed vessel, and the previously acutely hypoxic cells can once again be supplied with oxygen. This process is referred to as fast rexoygena- tion, and it has been observed to occur within minutes to hours (Ljungkvist et al, 2006; Redler, Epel & Halpern, 2014). Thus, it could be expected that the tumour oxygenation pattern will change locally between fractions so that cells that were hypoxic at one fraction become oxygenated at the next and vice versa. This type of reoxygenation is thus not dependent on that cells die or stop consuming oxygen.

20 Figure 2.3: Illustration of fast reoxygenation. The colour scale indicates the oxygen partial pressure as a result of diffusion from three blood vessels and consumption by the cells. In the left panel one of the blood vessels is occluded. In the right panel the previously occluded blood vessel is open.

2.3 Modelling tumour oxygenation and its inﬂuence on cell survival

The fate of an individual cell after irradiation is largely determined by its intrinsic sensitivity to radiation. The DNA molecule has been identified as the primary target for radiation, and a double-strand break (DSB) is considered to be the primary lethal lesion. However, it is not the creation of damage that defines the sensitivity, but rather the cell’s ability to repair this damage. This makes the concept of intrinsic radiosensitivity rather elusive, as a myriad of factors are likely to influence the recognition of DNA damage and the subsequent repair mechanisms activated by the cell (Szumiel, 2008). However, the difference in e.g. surviving fraction at 2 Gy (SF2) could be used to quantify the difference in radiosensitivity between different cell lines. Apart from variations in sensitivity between different cell types and the intrinsic properties of signalling and repair, external conditions such as the level of oxygenation clearly also impact on the effective radiosensitivity ultimately determining the radiation response of each cell. Thus, the tumour microenvironment is likely to strongly modify the outcome of a radiotherapy treatment, raising a question of the level of heterogeneity in sensitivity that needs to be considered in evaluating and predicting biological effects of radiation. It is in many ways convenient to think of tumours in analogy with normal tissues as uniform populations of cells that behave and respond similarly to e.g. radiation. In fact, this is generally assumed in the clinical application of many radiobiological models such as the well-established linear quadratic (LQ) model in its basic form (Barendsen, 1982), where entire organs and tumours are often described by single values of radiosensitivity. This assumption substantially

21 simpliﬁes treatment decisions based on the comparison of isoeffect fractionation schedules as assessed by the concept of biological effective dose (BED). In reality, the tumour microenvironment in terms of oxygenation and thus sensitivity is far from black and white and instead constitutes a non-linear gray scale of sensitivities. In order to fully understand and hence be able to predict the response of tumours, the overall paradigm must shift from considering static, uniform populations of cells to heterogeneous and dynamic. To this end, radiobiological modelling is the most powerful tool offering the testing and evaluation of a wide range of hypotheses which might not otherwise trans- late from bench to bedside very easily. There are various approaches to radiobiological modelling and thus also a range of models for radiation cell survival to consider. The perhaps most well-known is the linear quadratic (LQ) model (Barendsen, 1982) which is mathematically simple and has been given mechanistic interpretation (Chad- wick & Leenhouts, 1973). According to the LQ model, the surviving fraction SF in a well-oxygenated cell population after a single dose of radiation D is given as: SF = exp −αD − βD2 (2.1) where α and β are the radiosensitivity parameters for oxic conditions. For a hypoxic cell population, the dose-modifying effect from the lack of oxygen can be incorporated in the survival expression by adjusting the radiosensitivity parameters according to the OER:

α β SF = exp − D − D2 (2.2) hyp OER OER2

The survival in a mixed population can furthermore be calculated as:

SFmixed = foxSFox + fhypSFhyp (2.3)

where fox and fhyp are the fractions of cells in the oxic and hypoxic compartments respectively. In this way, tumour heterogeneity can be taken into account by considering a tumour model of arbitrary size consisting of a number of compartments with different oxygenation and hence different OERs (Carl- son, Stewart & Semenenko, 2006; Crispin-Ortuzar et al, 2017; Guerrero & Carlson, 2017).

22 While compartment modelling offers the opportunity to consider tumour heterogeneity in treatments using homogeneous dose distributions, some applications require that the spatial distribution of tumour heterogeneity is considered in relation to the dose distribution. For example, in the case of evaluating the impact of a heterogeneous dose as in SBRT, the spatial overlap between e.g. a resistant area and the escalated dose needs to be taken into account. Thus, by ﬁrst creating a model of a tumour and its oxygenation, the outcome of any dose distribution can be assessed taking into account the dose-modifying effect from the pO2 on voxel level by considering the OER as a continuous function of pO2 (Alper & Howard-Flanders, 1956; Toma-Dasu & Dasu, 2013). One example of a three-dimensional model of heterogeneous and dynamic oxygenation has been extensively described by Toma-Dasu and Dasu (2013).

Figure 2.4: Schematic illustration of the cross-section through the three- dimensional model of heterogeneous and dynamic tumour oxygenation. NB: The size and the density of the blood vessels not to scale.

Briefly, in this model the oxygenation is created based on inter-vessel distance (IVD) distributions that were experimentally derived from resin casts of tumour vasculature. Thus, the tumour volume is geometrically defined as a sphere of desired size, and an IVD distribution is assigned to the volume. Ves- sels are randomly positioned in the volume according to the mean value and the spread of the distribution, and the voxelised oxygenation is calculated with a desired resolution by solving a reaction-diffusion equation for the transport of oxygen in tissue. Acute hypoxia is simulated by assuming that a constant fraction of vessels are inactivated, and fast reoxygenation is simulated by randomly changing the position of these vessels. Chronic hypoxia can be simulated by geometrically defining a sub-volume and assigning an IVD distribution with

23 larger mean value and spread to it. Similarly, slow reoxygenation is simulated by geometrically shrinking the sub-volume and re-calculating the oxygen tension map (Toma-Dasu & Dasu, 2013). The importance of considering heterogeneity is also relevant with respect to assessing the applicability of cell survival models. For example, the LQ model has been criticised for overestimating cell kill in the high-dose range as compared to in vitro data showing a straight dependence on dose in a log-linear plot. A commonly quoted alternative is the universal survival curve (USC) model, which is a merge of the LQ model at low and intermediate doses, and the single-hit multi-target (SHMT) model at doses above a threshold dose DT (Park et al, 2008): −α − β 2 ≤ exp D D if D DT SF = (2.4) exp − 1 D + Dq if D ≥ D D0 D0 T

where Dq represents the dose where the tangent of the final slope -1/D0 of the survival curve intersects the horizontal axis on which the dose is indicated. In the case of heterogeneous oxygenation, the parameters Dq, D0 and DT are modified through the OER similar to equation (2.2). While there is an on-going debate regarding the suitability of the LQ model in the high-dose range (Kirkpatrick, Brenner & Orton, 2009), the USC model has also received some criticism based on its lack of a mechanistic basis (Tomé, 2009). Attempts to compare the two models have almost exclusively been made for uniform populations with respect to the oxygenation (Park et al, 2008). However, taking into account the modification in radiation sensitivity as introduced by the heterogeneous tumour oxygenation has shown that the two models predict similar results with respect to survival and tumour control probability (TCP) over a much larger dose-range than anticipated for a uniformly oxic tumour (paper I, Lindblom et al, 2014a). This effect was demonstrated using both a two-compartment model for the comparison of surviving fraction as predicted by the two models, and using the realistic three-dimensional model illustrated in figure 2.4 for the calculation of TCP. This demonstrates the importance of making relevant comparisons when assessing the clinical applicability of radiobiological models; evaluating models by fitting them to well-oxygenated in vitro data is probably not a realistic representation of in vivo human tumours. Furthermore, it is an example of how considering uniform versus heterogeneous cell populations can result in rather different conclusions. This may ultimately affect the clinical decisions made regarding the treatments of patients with tumours most likely far from homogeneously well oxygenated.

24 Furthermore, the inherent differences in the nature of the various types of hypoxia most likely imply another degree of complexity with respect to the radiosensitivity of hypoxic cells. Depending on whether hypoxia is a result of a temporarily collapsed vessel or of a chronic insufficiency in the vascular supply, the microenvironment will differ dramatically. Thus, while acutely hypoxic cells are likely to be radioresistant as a result of a temporary reduction in oxygen availability, the chronically hypoxic microenvironment is characterised by nutrient deprivation and insufficient waste removal. In this starved, acidic environment, an increased sensitivity could reasonably be expected. Such nu- ances will however not be accurately reflected in the survival expression in equation (2.2), which will predict the same survival for a given level of pO2 regardless of the other important aspects of the microenvironment. Against this background, alternative survival expressions for acutely and chronically hypoxic populations have been proposed (Denekamp & Dasu, 1999). These were based on the inducible-repair variant of the LQ model (Joiner & Johns, 1988), including an α parameter for the hypersensitive part of the survival curve, αS, which transitions to a resistant parameter, αR:

α − D S D 2 SF = exp −αR 1 + − 1 e C D − βD (2.5) αR

where DC corresponds to the dose at which 67% (1 − 1/e) of the transition to the maximum resistance has occurred. With the expression in equation (2.5) describing the surviving fraction in well-oxygenated tissue, the corresponding expression for acutely hypoxic cells could be obtained by simply modifying the sensitivity parameters αS, αR and DC as in equation (2.2):

α α D β R S − · = − + − DC OER − 2 SFacutely hypoxic exp 1 1 e D 2 D (2.6) OER αR OER

The maximum extent of inducible repair is described by the inducible repair ratio (IRR), αS to αR (Denekamp & Dasu, 1999). Thus, an increased radioresistance is reﬂected in a larger αS/αR, while the complete absence of inducible repair would correspond to an IRR of unity, reducing equation (2.6) to the reg- ular LQ expression modiﬁed for pO2. Considering that chronically hypoxic cells would have very little, or no repair capacity as a result of the depleted energy levels typical of a chronically hypoxic environment, the survival expression for this type of cells would correspond to:

25

α β SF = exp − s D − D2 (2.7) chronically hypoxic OER OER2

Thus, by using equation (2.5)-(2.7), another degree of heterogeneity can be introduced in modelling the response of hypoxic tumours. This could prove to be necessary in order to accurately predict the outcome of a radiotherapy treatment, which could in turn be crucial for the choice of treatment strategy. Depending on the proportion of chronically hypoxic cells, there could be a significant difference between considering different response characteristics for acutely and chronically hypoxic cells compared with assuming the same modification of the radiosensitivity for a given level of pO2. For a fractional volume of only about 6% assumed to be chronically hypoxic, the total dose corresponding to 50% tumour control probability (D50) for a 30 fractions schedule could be reduced by 10% if the survival is calculated using equations (2.5)-(2.7) compared with equation (2.2). For a treatment schedule delivering 3 fractions the difference becomes even more pronounced with a corresponding reduction of 30% (paper II, Lindblom et al, 2017). Disregarding the increased sensitivity of chronically hypoxic cells thus implies that a higher dose than necessary is expected to be required for a given level of local control. While this could be considered to be a conservative approach, it could also imply that e.g. a boost-dose to a hypoxic sub-volume is deemed infeasible, resulting in that the patient does not receive the most aggressive treatment, and, in worst case, even an insufficient one.

26 3. Time, dose and fractionation: radiobiological aspects and theoretical modelling

There currently exists a vast range of treatment schedules with respect to the total dose, number of fractions and overall treatment time. For example, head- and-neck tumour treatments range from extremely hypofractionated stereotactic schedules of one single fraction to conventionally- and hyperfractionated techniques (Kaanders, Bussink & van der Kogel, 2002; Amini et al, 2014). In addition to hypoxia, the pathophysiology of tumours is characterised by several other aspects with radiobiological implications, as is classically described by the 5 Rs of fractionated radiotherapy. Provided the tools for assessing individual radiosensitivity, it is realistic to expect that there is an optimal way of delivering radiotherapy within one speciﬁc tumour type. This inevitably indicates that the best treatment solution is yet to be found for many types of cancer treated with radiotherapy. By altering crucial parameters such as the overall treatment time, prescribed total dose and number of fractions, the radiobiological mechanisms in favour of a high biological effectiveness can be both promoted and counteracted as in a typical optimisation problem.

3.1 Fractionation

3.1.1 Sensitivity to fractionation The impact of altering the fractionation pattern on the response of a tissue can be described by the survival curve for that particular cell type. For the LQ model, the dose at which the contribution to cell kill from αD equals the contribution from βD2 is given by the ratio of α to β. This parameter represents a measure of the curvature of the survival curve, which in turn reflects the sensitivity of the specific tissue to fractionation. Quickly growing tumours tend to have a high α/β ratio reflecting a more straight survival curve, while late reacting normal tissues usually have lower α/β ratios corresponding to a more pronounced curvature. Provided the complete repair of sub-lethal damages between fractions, the shoulder of the survival curve is repeated at each fraction, leading to a differentiation between the survival of late and early reacting tissues that increases with increasing number of fractions.

27 Figure 3.1: Illustration of the differential in radiation response between late- (low α/β) and early- (high α/β) reacting tissues that increases with the number of fractions.

3.1.2 Inter-fraction reoxygenation

Apart from the benefit of multifraction treatment schedules as presented by the differential in tumour- and normal tissue repair capacity, fractionated treatments offer another major benefit through the reoxygenation of hypoxic cells. As previously discussed, this can occur between fractions as a result of fast reoxygenation, and over time as a result of the well-oxygenated cells being eradicated leading to a global increase in the tumour oxygenation. When moving from conventional radiotherapy to hypofractionated treatments such as SBRT, the impact of inter-fraction fast reoxygenation becomes increasingly important since little improvement in the global tumour oxygenation can be expected in the limited overall treatment time. In fact, significantly reducing the number of fractions – as has been the tendency in photon SBRT – is very likely to limit the achievable level of control in hypoxic tumours. By allowing for fluctuations in the oxygenation on the microscale between fractions, the overall effective radiosensitivity of the tumour is increased even if the hypoxic fraction remains constant throughout the treatment. This could make the difference between a clinically feasible total dose that is curative, and one that is not (paper III, Lindblom et al, 2014b).

28 3.2 Dose per fraction

A higher dose per fraction implies a smaller number of fractions and thus also an increased biological effectiveness of the treatment. In addition, the dose per fraction itself may imply several interesting mechanisms possibly beneﬁting the treatment outcome. For example, a decreased impact of variations in sensitivity within the cell cycle could be expected, as high doses of radiation are likely to cause permanent cell cycle arrest (Park et al, 2000). Furthermore, it has been hypothesised that damage to the tumour vasculature might pose a secondary effect in SBRT, occurring only at high doses (Park et al, 2012). This is mainly based on the assumption that tumour cells not targeted by the radiation might still die as a result of their vascular supply disintegrating, which poses an interesting opportunity that might be of therapeutic relevance in stereotactic treatments. In a mouse study from 2015, Song et al found a dose-dependent secondary cell death occurring only after a single dose of 15-30 Gy. This was attributed to vascular damage, accompanied by an observed increase in hypoxia resulting from the deterioration of the vasculature (Song et al, 2015). Another interesting phenomenon observed in some cases of SBRT treatments is the response in lesions not targeted by the radiation. This has been termed the abscopal effect and has been found to occur in several patients with metastatic melanoma treated with the monoclonal antibody ipilimumbab and, at some point, hypofractionated radiotherapy (Hiniker, Chen & Knox, 2012; Postow et al, 2012; Postow, Callahan & Wolchok, 2012). Stamell et al (2013) observed no response to chemotherapy in a primary melanoma, while localised radiotherapy to the primary lesion resulted in response in both the primary lesion and complete resolution of all (un-treated) in-transit metastases after eight months. This abscopal effect was associated with post-radiation anti- melanoma anti-bodies, which could imply that a systemic anti-tumour immune response was induced by the irradiation (Stamell et al, 2013). While an abscopal effect has been observed in other tumour types as well, such as metastatic renal cell carcinoma (Wersäll et al, 2006), more data is needed to conclude whether this is an effect particularly pronounced in stereotactic treatments, possibly offering a new dimension to SBRT in combination with immunother- apy (Popp et al, 2016). A possible drawback of large doses per fraction is the potentially increased treatment time per fraction. If the repair half-life of the tumour cells is shorter than, or comparable to, the time it takes to deliver the fraction, the increase in biological effectiveness from the higher fractional dose could be counteracted by the intra-fraction repair of tumour cells. This provides another reason to consider multiple instead of e.g. single-fraction treatments, which would also imply more time for inter-fraction fast reoxygenation. Interestingly, a

29 combined effect from the reduction in intra-fraction repair and the increase in reoxygenation when increasing the number of fractions has been demonstrated (paper IV, Lindblom et al, 2015). For a limited range of fractions, the total isoeffective dose was reduced despite the fact that the number of fractions was increased. The lower fractional dose, (and hence shorter treatment time), and increased number of fractions thus completely counteracted the expected increase in total dose required for isoeffect due to inter-fraction repair of sub- lethal damages. This is one example showing the importance of considering several radiobiological aspects and their separate and combined impact on the effect of the treatment. Such studies require that individual parameters such as repair and reoxygenation can be isolated, which is essentially only possible in radiobiological modelling.

3.3 Overall treatment time

While the outcome of conventionally fractionated schedules can benefit from both fast and slow reoxygenation, treatments extending over several weeks are also subject to the issue of accelerated repopulation. As the tumour cells are successively eradicated, an accelerated proliferation is triggered after about three to four weeks of treatment (Withers, Taylor & Maciejewski, 1988; Begg, Hofland & Kummermehr, 1991; Kim & Tannock, 2005). For this reason, any gaps in the treatment due to e.g. sickness or holiday have to be compensated by an increase in the total dose, the magnitude of which can be calculated with the BED formula including a term for the proliferation rate (Fowler, 2001). It is thus beneficial too keep the treatment time as short as possible, while not compromising normal tissue toxicity or tumour control. In SBRT, the delivery of few, high-dose fractions is feasible due to the more rigid fixation and thus reduced target margins, allowing for rather steep dose gradients within the target volume (Yang et al, 2010). Another strategy is accelerated hyperfractionation, i.e. several fractions of smaller dose per day in a shorter overall treatment time. In continuous hyperfractionated accelerated radiotherapy (CHART), three daily fractions every day for 12 days were delivered. While CHART resulted in improved local control and survival compared with conventional radiotherapy in NSCLC, no significant benefit in tumour control or survival compared with CFRT was found in head-and-neck cancer (Dische et al, 1997). Interestingly, standard hyperfractionation, in which the treatment time is not reduced, has however shown overall better results in these tumours (Bourhis et al, 2006; Beitler et al, 2014).

30 3.4 Heterogeneous fractionation patterns

While the radiobiological rationale for fractionation is quite clear from the early- to late-reacting tissue differential in α/β and the importance of inter- fraction reoxygenation, extreme hypofractionation is an attractive approach as the practical (and in some cases economic) beneﬁts from a short treatment in- volving few fractions are obvious from several points of view. Elderly patients suffering from co-morbidities – as is often the case for e.g. NSCLC patients – will prefer a short a treatment as possible, and the clinic can increase their throughput by freeing valuable accelerator time. Furthermore, the biological effectiveness according to the BED formula will indeed be higher for an SBRT treatment as compared to CFRT, while not necessarily escalating the normal tissue toxicity to an unacceptable level (Yang et al, 2010).

Figure 3.2: Schematic illustration indicating the impact of different fractionation patterns on local control in hypoxic tumours. The red curve shows the beneﬁcial effect of fast reoxygenation, which will be most pronounced when going from single-fraction treatments to schedules employing few fractions. The orange curve shows the impact of slow reoxygenation, which is expected to contribute to an increase in local control after a couple of weeks of treatment. The green curve corresponds to the impact of repair during the delivery of one fraction, which rapidly decreases as the time to deliver each fraction becomes small in relation to the half-life of repair. Starting at three to four weeks of radiotherapy, the accelerated repopulation of tumour cells will impact negatively on the probability of controlling the tumour as reﬂected by the blue curve.

31 In light of the success of SBRT, hypofractionated schedules are indeed encour- aging to pursue, especially for cancer types such as NSCLC in which conventionally fractionated treatments have failed (Onishi et al, 2004). However, the inevitable reservation necessary for extremely hypofractionated regimens as applied to hypoxic tumours raises the question of whether there is another approach that will increase rates of local control even further. While both slow and fast reoxygenation can be expected to benefit local control in conventionally fractionated treatments, the overall treatment time in CFRT often extends beyond the kick-off time for accelerated repopulation. In hypofractionated SBRT, this can be completely avoided at the cost of a much reduced if not zero benefit from slow reoxygenation, given that the sufficiently oxygenated irradiated cells will not die or immediately stop consuming oxygen (Hall & Giaccia, 2012). If the entire treatment is given in only one fraction, there will be no benefit at all from any kind of reoxygenation. Dividing the treatment into three or five fractions could thus be expected to have a rather large impact on the achievable level of local control as a result of the inter-fraction fluctuations in acute hypoxia (paper III, Lindblom et al, 2014b). Thus, while the optimal solution is probably not to be found at either of the extremes of the fractionation scale, an in-between approach employing heterogeneous fractions of dose could be worth investigating. Such a schedule could, in addition to taking advantage of the fast reoxygenation and short overall treatment time, imply an increased biological effectiveness compared to homogeneously fractionated SBRT. For example, if a 60 Gy treatment is delivered in 2 Gy fractions with one fraction per day, five days per week, the overall treatment time is 40 days. If instead a heterogeneous fractionation pattern is considered, in which the same total dose is instead given in 9 fractions of 4 Gy and 12 fractions of 2 Gy, the treatment time is reduced to 28 days. In a modelling study, such a change was found to increase the tumour control probability from 32% to 100% (paper IV, Lindblom et al, 2015). Apart from the potential improvement in treatment outcome based on an increased dose per fraction, the time-points at which the escalated fractions are delivered could further impact on the local control. For example, it could be hypothesised that delivering larger fractions on e.g. Mondays and Fridays could (partly or entirely) compensate for the lack of treatment during the weekend.

32 4. Clinical aspects and implications of tumour hypoxia

Ever since the discovery of the oxygen effect and the subsequent realisation of its implications in radiotherapy (Gray et al, 1953; Thomlinson & Gray, 1955), there has been an ongoing pursuit of therapeutic methods to overcome the increased radioresistance associated with hypoxia. The early explored strategies were based on sensitising the tumour, either by increasing the tumour oxygenation as in hyperbaric oxygen therapy (Mayer et al, 2005), or by administering an oxygen-mimetic chemical compound specifically accumulating in hypoxic cells. Upon irradiation and subsequent DNA damage, the compound would act as oxygen and hence increase the radiosensitivity. This was the first clinical use of nitroimidazoles as hypoxia-targeting drugs in the 1970s, starting with metronidazole in 1973 followed by misonidazole in 1974. However, the clinical results were disappointing, as the drug doses that could be administered at an acceptable level of toxicity were too low to accomplish a radiosensitising effect (Dische, 1985). In 1979, Chapman suggested an alternative use of the hypoxia-sensitising agents as markers for hypoxia (Chapman, 1979). For this purpose, the toxicity previously observed would not be an issue as the amount of the drug required would be much less than for therapeutic purposes. Fol- lowing the first demonstration of histological assessment of tumour hypoxia with radiolabelled misonidazole and autoradiography (Chapman, Franko & Sharplin, 1981), this and subsequent work has paved the way for the development of the abundance of hypoxia-specific radiolabelled tracers that are used in positron emission tomography (PET) today. Despite the many years of work dedicated to the issue of hypoxia in particular, little progress has been made in introducing routine pre-treatment assessment of tumour oxygenation into the clinical practice. Similarly, a gold standard for targeting tumour hypoxia in the therapeutic setting is yet to be identified. From a pure physics’ point of view, the two most straightforward ways of overcoming the radioresistance associated with tumour hypoxia would be to either escalate the dose (according to the OER), or to increase the LET (and thereby increase the relative contribution to damage creation from the direct mode of action). However, early explored strategies were primarily focused on manipulating the tumour environment, given the natural limitations in technically and clinically achieving either of the above. In addition, these approaches do not a priori take into account the possibility that the oxygena-

33 tion, and other crucial aspects of the tumour microenvironment, can change throughout the course of therapy. In fact, while the state-of-the art in hypoxia targeted radiotherapy, e.g. dose- and LET painting, is indeed based on these most primitive ideas of hypoxia mitigation, their success is most likely entirely dependent on some key physiological processes altering the oxygenation of the tumour during therapy (Antonovic et al, 2014; paper III, Lindblom et al, 2014b; paper VI, Ureba et al, 2017). The existence and importance of such processes were in turn largely indicated by the initial clinical attempts of overcoming hypoxia.

4.1 Overview of strategies to overcome hypoxia

4.1.1 Sensitising techniques Following the pioneering work by Thomlinson and Gray, the use of hyperbaric oxygen therapy in patients treated with radiotherapy was tested for the first time in 1955 (Churchill-Davidson et al, 1955). In this experimental study, an increase in tumour tissue damage irradiated at three times atmospheric pressure was observed in a majority of the patients treated. Since then, a therapeutic benefit from hyperbaric oxygen therapy in combination with radiotherapy has however not been unequivocally confirmed. The collected experience include both observations of no benefit from combining the two treatments, and of a significant improvement in local control compared with using radiotherapy alone (Mayer et al, 2005). It has also been suggested that the observed outcome, including adverse effects, may rather be attributed to the unusual fractionation schedules employed in these studies (Bennett et al, 2008). Another tumour-sensitising strategy receiving increased attention during the 1970s is hyperthermia in combination with radiotherapy. In addition to a sensitising effect if combined with radiotherapy, hyperthermia was also considered to have a direct cytotoxic effect on cells in a microenvironment characterised by nutrient deprivation and increased acidity (Overgaard, 1989a). Un- like the hyperthermic radiosensitisation, this cytotoxic effect would be limited to tissues with an insufficient blood supply, giving rise to a microenvironment particularly exposed to the detrimental effects from hyperthermia. As these regions would typically also be hypoxic and thus radioresistant, hyperthermia in combination with radiotherapy was considered to be a treatment option for hypoxia mitigation in particular (Overgaard, 1989b). In 1995, it was proposed that the positive outcome from combining hyperthermia treatment with fractionated radiotherapy was rather an indirect effect of reoxygenation (Ole- son, 1995), resulting from heat-induced vessel dilation and hence increased

34 perfusion. In a review from 2004, Vujaskovic and Song suggested reoxygenation to be dominant over a direct cytotoxic effect for the thermal doses easily achievable in the clinic (Vujaskovic & Song, 2004). More recent studies have demonstrated additional effects from hyperthermia of particular interest for the purpose of combination with radiotherapy, such as inhibition of DNA damage repair (Krawczyk et al, 2011; van den Tempel et al, 2017). However, a widespread clinical use of hyperthermia has not been established, potentially attributable to remaining question marks in regard to the clinical beneﬁt as well as technical challenges of delivering the treatment in conjunction with e.g. radiotherapy (Peeken, Vaupel & Combs, 2017). The initial experience with the hypoxia-dedicated radiosensitising agents introduced in the 1970s was overall disappointing (Overgaard, 1989a), as the 2-nitroimidazole compounds (e.g. misonidazole) could never be introduced into the clinical routine as a result of unacceptable levels of toxicity (Over- gaard, 2007). More promising results have been obtained with the 5-nitroimidazole nimorazole, which was found to signiﬁcantly improve the outcome of conventionally fractionated radiotherapy of supraglottic larynx and pharynx carcinoma (Overgaard et al, 1998). However, routine clinical use of hypoxic ra- diosensitisers such as nimorazole is currently limited to Denmark (Mortensen et al, 2012) where, interestingly, the standard treatment for head and neck cancer patients include nimorazole as concomitant treatment to accelerated radiotherapy and weekly chemotherapy (Christiaens et al, 2017).

4.1.2 Dose and fractionation techniques

While the early attempts at hypoxia mitigation hitherto described have largely been inconclusive, they have indicated the importance of fractionation, and that the best approach could be one that targets several factors at the same time. One such example is ARCON, accelerated radiotherapy with carbogen and nicotinamide (Rojas, Joiner & Denekamp, 1992; Zackrisson et al, 1994; Kaanders, Bussink & van der Kogel, 2002), which was used for the treatment of laryngeal cancer. Accelerated repopulation is counteracted by delivering more than one fraction per day and thus effectively shortening the overall treatment time, combined with the co-administration of nicotinamide, to dilate vessels and thus counteract the perfusion-limited hypoxia, and carbogen, to decrease the diffusion-limited hypoxia. High rates of local and regional control have been obtained using this technique in head-and-neck cancer (Kaanders et al, 2002), but only the regional control was improved compared to accelerated radiotherapy in laryngeal cancer (Janssens et al, 2012). In this study, the improvement in regional control was however found to be particularly pronounced in hypoxic tumours.

35 The lack of success in the various attempts of sensitising hypoxic tumours has instead largely focused the research in hypoxia mitigation in radiotherapy on ways of increasing the biological effectiveness of the treatment. By increasing the physical dose, the dose-modifying effect of hypoxia can be counteracted as quantified by the OER. However, the magnitude of the dose-escalation is natu- rally limited by the normal tissue toxicity, and implies that only sub-volumes of the tumour could be targeted in this way. Given the technical advances in dose delivery techniques and the ability of imaging tumour physiology, the concept of dose painting was introduced by Ling and colleagues in 2000. They suggested that functional imaging could be used to identify biological target volumes (BTVs) to be incorporated into the treatment planning process (Ling et al, 2000). The feasibility of this approach was later demonstrated in a planning study, where the dose to a tumour sub-volume identified using PET imaging could be escalated without compromising the normal tissue sparing (Chao et al, 2001). Since then, several studies on dose painting have been published, and different ideas on how to define the boost-volumes have been proposed. Thus, sub-volumes defined based on a threshold with respect to the relative uptake of a radiolabelled tracer have been given a homogeneous dose that is escalated relative to the rest of the gross target volume (GTV), while more heterogeneous dose prescription has also been suggested (Bentzen, 2005) Similarly, another basic strategy to counteract the impact of hypoxia with respect to the fundamental physics of the interactions of ionising radiation with matter is using high-LET radiation. This is based on the negligible oxygen effect in the pristine Bragg peak where the LET is at its highest. However, in order to achieve target coverage, a spread-out Bragg peak (SOBP) is used as obtained from the superposition of particles with different energies and hence ranges in tissue. In the SOBP, the LET can be substantially lower than in the Bragg peak of monoenergetic beams, and the resulting (dose averaged) LET in the tumour is non-uniform and can exhibit rather wide ranges of LET. For example, the LET in the SOBP in carbon ion radiotherapy can range between 30 and 80 keV/μm (Antonovic et al, 2014), corresponding to an OER between 2 and 3 (Furusawa et al, 2000). Thus, assuming that the tumour oxygenation is irrelevant to the outcome of high-LET radiotherapy might be dangerous when designing such treatments with respect to the prescribed dose and number of fractions. Indeed, only a limited reduction of the oxygen effect using carbon ions was found based on in vitro data (Bassler et al, 2010), and LET painting with carbon ions could only achieve satisfactory levels of tumour control probability in hypoxic sub-volumes of very limited size (Bassler et al, 2014). Al- though the importance of considering intra-tumour heterogeneity in this context has been highlighted, fractionation effects including reoxygenation have largely been regarded as of less importance in ion therapy (Tinganelli et al,

36 2015). This effectively limits the clinical applicability of the results to scenarios in which part of the tumour remains consistently hypoxic and resistant throughout the treatment. The impact of considering ﬂuctuations in radiosensitivity has been demonstrated by modelling the impact of local changes in the oxygen tension distribution between fractions of carbon ion therapy, corresponding to fast reoxygenation of acute hypoxia. Taking into account a heterogeneous oxygenation and LET distribution, the tumour control probability (TCP) of realistic in silico tumours was found to be inﬂuenced by the presence of hypoxia, and that allowing for reoxygenation through fractionation enables a range of schedules to be applied resulting in high levels of tumour control (Antonovic et al, 2014). Similar modelling studies have demonstrated the substantial reduction in the total dose required for a given level of TCP if a dynamic tumour oxygenation is taken into account (paper III, Lindblom et al, 2014b; paper IV, Lindblom et al, 2015). This is most relevant for dose painting studies as well, as the relative increase in dose necessary to a sub-volume that is assumed to be consistently hypoxic throughout the treatment could very likely be too high to comply with normal tissue toxicity constraints. Considering these effects could ultimately make the difference between a hypoxia-targeted treatment plan that is clinically feasible and one that is not. Thus, whether hypoxic sub- volumes are targeted with dose, LET, or a combination thereof, fractionation effects should not be ignored. Regardless of the therapeutic strategy pursued, any kind of targeted treatment must be based on the pre-treatment assessment of the tumour microenvironment.

4.2 Overview of methods to measure tumour oxygenation

As the issue of hypoxia has concerned the scientiﬁc and medical community for more than a century, there exists a vast collection of methods to measure hypoxia. These can in general be characterised as invasive to various extents, and practically or completely non-invasive. Furthermore, while the techniques that can be characterised as invasive are often referred to as able to provide ab- solute measurements, the less- or non-invasive techniques typically represent a surrogate for measuring the actual oxygen partial pressure.

4.2.1 Direct and invasive methods Although it is no longer available for purchase, Eppendorf histography is often referred to as the ’gold standard’ for measuring tumour oxygenation. The technique is based on a polarographic needle being inserted into the tissue,

37 and measuring the oxygen tension through electrochemical reduction. Dur- ing measurement, the probe is automatically moved enabling measurements at several points along each needle track. Apart from being invasive and thus limited to superficially located tumours, the probe consumes oxygen and thereby disturbs the microenvironment. Furthermore, although measurements can be made at several positions in the tumour, a complete and detailed view of tumour oxygenation cannot be obtained. Although the method has been crucial in demonstrating the impact of hypoxia on the treatment outcome (Vaupel, Kallinowski & Okunieff, 1989; Nordsmark et al, 2005), it has not been suc- cessful in predicting treatment outcome on an individual basis. The time-resolved fluorescence-based optical sensor (commercially known as OxyLiteTM, Oxford Optronix Ltd., Oxford, UK) also requires a probe to be inserted into the tissue. In this technique, a luminophoric compound incorporated into the tip of the probe gives rise to fluorescence light as a result of being irradiated with blue light supplied through a fiber optic cord. As the luminophoric compound molecules in the probe collide with oxygen molecules in its immediate vicinity, the emission of fluorescence light is quenched at a rate that is inversely proportional to the oxygen partial pressure. Although this technique is thus also invasive, it has a major advantage over the polarographic needle in that it does not result in any consumption of oxygen (Jarm et al, 2002). However, given the fact that the probe measures the average oxygen tension in its immediate vicinity, comprehensive and detailed information on the heterogeneity of tumour oxygenation cannot be obtained. A less invasive technique is offered by electron paramagnetic resonance (EPR) oximetry, which is based on the relaxation of paramagnetic species with unpaired electrons. While molecular oxygen in itself is paramagnetic, an oxygen EPR spectrum cannot be directly obtained in biological systems as the signal is too weak. However, oxygen can affect the relaxation of other paramagnetic species which can be injected. Thus, through collisions between the injected paramagnetic probe and molecular oxygen, the relaxation of the probe is enhanced at a rate that is proportional to the oxygen concentration (Gallez, Baudelet & Jordan, 2004). Unlike the polarographic needle, EPR measurements can be repeated several times in the same measurement point. This enables the assessment of the dynamic character of the oxygenation (Gallez et al, 2017). In addition to measuring tumour oxygenation, EPR is also used in the clinic for the assessment of e.g. peripheral vascular disease and wound healing (Swartz et al, 2014).

38 4.2.2 Indirect and non-invasive methods A non-invasive optical method is offered by near-infrared spectroscopy (NIRS). This method is based on the absorption spectrum of hemoglobin in the near- infrared wave length range (700-1100 nm), in which the light is able to pen- etrate tissues including bone. A light-emitting probe is placed onto the tissue of interest, and measures the spectrum of the light that is eventually reﬂected back to the probe. As the optical spectra of oxyhemoglobin differs from that of deoxyhemoglobin, a measure of the tissue oxygenation can be provided (Scheeren, Schober & Schwarte, 2012), although the scattering of the light in the tissue implies a signiﬁcant degradation of the resolution (Lee, Boss & De- whirst, 2014). The perhaps most commonly used modalities for functional imaging is magnetic resonance imaging and positron emission tomography. In spite of the injection of e.g. a contrast agent or a radiolabelled tracer, these techniques are generally considered to be non-invasive and can provide static as well as dynamic images with high resolution.

Functional magnetic resonance imaging The abundance of imaging protocols in magnetic resonance imaging (MRI) provides several possibilities of enhancing the signal associated with various physiological aspects of a tissue that could enable both the detection and the characterisation of pathology. For example, diffusion-weighted imaging (DWI) reflects the motion of water molecules in tissue, which is highly dependent on the microenvironment with respect to e.g. the density of cells (Khoo et al, 2011). Given the increased rate of proliferation typically associated with tumours, the intra-tumour diffusion could reasonably be expected to be more restricted than in normal tissue, enabling the detection of tumours (Koh & Collins, 2007). In addition to the increased cell density, the interstitial fluid pressure typically builds in tumours as a result of an insufficient and abnormal vasculature, which also causes hypoxia (Rofstad, Galappathi & Mathiesen, 2014). A more pronounced reduction of the water diffusion in such areas thus implies a possibility to further characterise the tumour as e.g. hypoxic using DWI, which has also been demonstrated in brain tumours (Hino-Shishikura et al, 2014). In 1990, Ogawa and colleagues observed that the contrast in magnetic resonance images of rat brain obtained at high magnetic fields was enhanced by the appearance of dark lines in a pattern similar to the blood vessel geometry. The appearance of the lines was dependent on the oxygen concentration in the blood, and disappeared if the blood was well oxygenated (Ogawa et al, 1990a). This effect was explained by the paramagnetic character of deoxyhemoglobin,

39 which gives rise to a blood-oxygen-level-dependent (BOLD) contrast in magnetic resonance images obtained using gradient-echo techniques at high field strengths (Ogawa et al, 1990b). Today, in vivo assessment of brain function in neuroscience research is primarily done using BOLD-MRI (Mark, Mazerolle & Chen, 2015), which has also been used to assess the oxygenation in other normal tissues (Prasad, Edelman & Epstein, 1996) as well as in tumours (Hal- lac et al, 2012). Since MRI is both non-invasive and free from ionising radiation, examina- tions such as DWI and BOLD-MRI could be repeated over time to assess temporal fluctuations. However, there are also methods to directly assess the dynamic character of e.g. the tumour vasculature. In dynamic contrast-enhanced (DCE) magnetic resonance imaging, images are acquired before, during and after the administration of a contrast agent (typically gadolinium). In this way, the kinetics of the contrast agent can be studied, reflecting both vascular per- meability and perfusion (Choyke, Dwyer & Knopp, 2003). Furthermore, it has been proposed that a combination of DCE-MRI and T2*-weighted gradient- echo MRI could be used to assess the spontaneous fluctuations in blood flow and oxygenation associated with acute hypoxia (Baudelet et al, 2004).

Positron emission tomography

Today, several tracers are available for imaging hypoxia with positron emission tomography (PET). The majority of them belong to the 2-nitroimidazole fam- ily, originally considered for the purpose of radiosensitisation, and are labelled with the positron-emitting isotope 18F. The most commonly used and widely available hypoxia-specific tracer is 18F-fluoromisonidazole (18F-FMISO) (Ra- jendran & Krohn, 2015), which has been used to evaluate tumour hypoxia before and during treatment (Koh et al, 1995; Rasey et al, 1996), as well as for predicting treatment outcome (Eschmann et al, 2005). However, FMISO has a slow clearance from normoxic tissues, resulting in a high background signal. This has stimulated the search for alternative tracers with more preferable clearance kinetics resulting in higher image quality. Among the more promising ones, 18F-fluoroazomycin-arabinozide (18F- FAZA) has exhibited an improved tumour-to-blood ratio compared with FMISO (Carlin & Humm, 2012). Following the first use of FAZA for in vivo assessment of tumour oxygenation, the applicability of the tracer for dose painting in head-and-neck cancer has also been demonstrated (Grosu et al, 2005, 2007; Mortensen et al, 2012). Other nitroimidazole tracers studied for the purpose of hypoxia assessment include 18F-fluoroetanidazole (18F-FETA) (Barthel et al, 2004), 18F-fluoroerythronitroimidazole (18F-FETNIM) and 18F-2-nitroimidazol- pentafluoropropyl acetamide (18F-EF5). A clear advantage of FETA and FET-

40 NIM over FMISO has not been established (Carlin & Humm, 2012; Krohn, Link & Mason, 2008), and FETNIM has even been found to be inferior to FMISO in terms of tumour-to-normal tissue uptake ratio (Wei et al, 2016). While EF5 is technically more challenging to produce, it has been found to be predictive of the response to fractionated radiotherapy in pre-clinical models (Carlin & Humm, 2012; Ali et al, 2015). A relatively new, yet rather thoroughly studied nitroimidazole tracer developed for the purpose of overcoming some of the limitations associated with FMISO is 18F-Flortanidazole (18F-HX4). This tracer has a lower lipophilici- ty than FMISO, which implies an improved clearance from normoxic tissues. However, the uptake pattern of HX4 has been found to be similar to that of FMISO (Carlin et al, 2014; Wack et al, 2015), and the tumour-to-blood ratio to distinguish between hypoxia and normoxia that is used for FMISO has been adopted for HX4 (Zegers et al, 2013, 2016). While the use of HX4 in e.g. dose painting has been successfully tested (Even et al, 2015), a clear advantage of HX4 over FMISO is yet to be proven (Challapalli, Carroll & Aboagaye, 2017). The copper-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM) tracer has a completely different uptake mechanism than the nitroimidazoles, and can be labelled with several different copper isotopes with various half-lives including 60Cu, 62Cu, and 64Cu, the latter being the most commonly used (Vavare¯ & Lewis, 2007). Despite the high image contrast that can be obtained with this tracer, the uptake of 64Cu-ATSM in vitro was found to be cell-line dependent (Burgman et al, 2005). This ﬁnding has been conﬁrmed in an animal model, where an uptake that was dependent on tumour type was observed (Li et al, 2016). Thus, while the spatial distribution of 64Cu-ATSM was found to be stable during radiotherapy in canine patients (Bradshaw et al, 2014), further research on the potential of this tracer to assess tumour hypoxia is needed.

4.3 Hypoxia dose painting

In addition to pre-treatment identification of patients who would benefit from e.g. dose painting, any (truly) targeted treatment must also be preceded by adequate identification of the tumour sub-volume that should be targeted. In principle, dose painting studies have been based on one of two approaches in regards to the definition of the boost-dose volume and subsequent dose prescription. On the one hand, thresholding of the standardised uptake value (SUV) in the target has been performed to create the hypoxic boost-dose volume, which has subsequently been given a homogeneously escalated dose. This approach is commonly referred to as dose painting by contours (DPBC). On the other hand, in dose painting by numbers (DPBN), the heterogeneity in tracer uptake on voxel level has been considered within the whole GTV to cre-

41 ate an increasing dose-escalation based on a linear correlation between uptake and boost-dose (Meijer et al, 2011). While the DPBN approach has been proven technically feasible in some selected cases (Alber et al, 2003; Thorwarth et al, 2007), the most pressing question is to which extent the heterogeneity in PET images should be considered with respect to the stability of the boost-dose volumes over the course of treatment. This question is highly relevant in the context of tumour hypoxia, which is expected to change not only over time, but randomly from one fraction to another at cellular level. Repeated FMISO-PET imaging has demonstrated stable as well as both decreasing and increasing hypoxia in head-and-neck cancer during radiochemotherapy (Bittner & Grosu, 2013; Zips et al, 2012), with a geometric mismatch in hypoxic volumes during treatment of more than 20% (Zschaeck et al, 2015). In another patient cohort, an overall geographical stability in hypoxia was found in a majority of 16 head-and-neck cancer patients imaged with FMISO-PET before and during radiochemotherapy, while in five patients the hypoxic sub-volume was found to both increase and decrease in size (Bittner et al, 2013). In cervical cancer, no spatio-temporal stability in hypoxic sub-volumes identified on repeated FMISO-PET imaging during treatment was found, warranting that dose painting in this patient group should be pursued with caution. Meanwhile, repeated FAZA-PET imaging of lung cancer patients during the first three weeks of radiotherapy showed spatially stable uptake distributions in the tumour with overall strong correlation between the images as assessed on both volume- and voxel level (Di Perri et al, 2017). However, given the dose-modifying effect of hypoxia and the possibility of pre-treatment assessment of tumour oxygenation, dose painting is considered to be an attractive strategy for hypoxia mitigation. Several studies on hypoxia dose painting have investigated the feasibility of DPBC (Chang et al, 2013) as well as DPBN (Thorwarth et al, 2007), and while most studies are based on FMISO-PET imaging, other tracers such as FAZA and Cu-ATSM have also been used (Flynn et al, 2008; Grosu et al, 2007). Considering the uncertainty in the spatial and temporal stability of hypoxic sub-volumes, DPBC has been proposed as the more robust and clinically feasible approach (Thorwarth & Alber, 2010).

4.3.1 Deﬁning a hypoxic target volume

The majority of hypoxia-targeted dose-escalation studies have segmented the hypoxic target volume (HTV) according to an uptake threshold relative to a reference region that is considered to be well-oxygenated. For example, a tumour-to-muscle ratio (TMR) or tumour-to-blood ratio (TBR) has been used to deﬁne the threshold above which voxels are considered to be hypoxic. For

42 FMISO, a relative uptake ratio of 1.4 has been commonly used (Koh et al, 1995; Rasey et al, 1996), which has also been employed for HX4 (Zegers et al, 2013, 2016). While considering the relative uptake may very well be an adequate method to geometrically identify the boost-volume, the boost-dose required to counteract the hypoxia contained in the volume cannot be directly obtained from the uptake. The boost-doses considered so far have thus been chosen mostly with respect to what is feasible to deliver rather than what is actually needed in order to overcome the increased radioresistance (Even et al, 2015). If the boost-dose is too low, such an approach could result in the potentially misleading conclusion that hypoxia dose painting does not work. This could be further emphasised by disregarding the heterogeneity in uptake within the volumes, which is also very likely to vary between patients. Thus, two patients could have a similar HTV defined by an uptake threshold of e.g. 1.4, but with different uptake patterns – and thus different degrees of hypoxia – within their respective volumes. If both of them are given the same boost-dose, the outcome is likely going to differ depending on the size of the dose, and the degree of variability in radiosensitivity. While the relative uptake cannot be directly correlated with the boost-dose required, a relationship between pO2 and the dose-modifying effect can be assumed and incorporated into a survival model. Against this background, an alternative approach to segmenting the HTV has been proposed, in which the normalised tracer uptake is first converted to oxygen partial pressure, pO2 (Toma-Dasu, Dasu & Brahme, 2009a) as illustrated in figure 4.1. The mathematical function describing this conversion depends on a set of parameters that were derived for FMISO based on experimental data of uptake and pO2 measurements (Toma-Dasu, Dasu & Brahme, 2009b). Following such conversion, the threshold for defining the hypoxic target volume can be based directly in terms of pO2, where the level corresponding to hypoxia has been well-established as 5-10 mmHg (Vaupel, Höckel & Mayer, 2007). Furthermore, the calibration in terms of the oxygen partial pressure allows for the quantification of hypoxia, including the differentiation of pO2 within the HTV. Given the relationship between pO2 and dose- modification with respect to the biological effectiveness of a given physical dose, this is of high relevance for designing individualised treatment plans with high probability of success (Toma-Dasu, Dasu & Brahme, 2009a). The function developed for converting FMISO uptake to pO2 has subsequently been applied on HX4-PET images of non-small cell lung cancer patients, and was able to identify HTVs in good geometrical agreement with the corresponding volumes based on a TBR of 1.4 (paper V, Lindblom et al, 2017). The pO2-HTVs with highest relative overlap with the uptake-HTVs were all found within a range of 30-40 mmHg. Although the range was hence limited to

43 10 mmHg, the pO2 thresholds corresponding to the highest overlap were thus substantially higher than what is normally considered as hypoxic. The promising overall performance of the function therefore warranted the pursuit of new parameters that would preserve the geometric agreement whilst improving the calibration of pO2 levels. Based on the assumption that the uptake pattern of HX4 is similar to that of FMISO (Carlin et al, 2014; Wack et al, 2015) and that a TBR of 1.4 should correspond to 10 mmHg (Vaupel & Mayer, 2014), a new function was derived that was able to identify pO2-based HTVs in good agreement with uptake-based HTVs for a threshold of 10 mmHg (paper VI, Ureba et al, 2017).

Figure 4.1: Illustration of conversion from standardised uptake value (SUV, left) to pO2 (right) through the application of a conversion curve (middle).

4.3.2 Dose prescription based on imaging hypoxia

Similar to the fact that the pO2 distribution within the HTV is expected to differ between patients, it is also expected to change over time within one single patient. In CFRT, the continuous eradication of well-oxygenated cells lead to an overall improvement in the tumour oxygenation over the course of the treatment (Kallman, 1972). This can however not be expected in hypofractionated treatments, which are completed in a shorter overall time. For such schedules, the prescribed boost-dose needs to be designed under the assumption that the hypoxic fraction remains constant. However, fluctuations in acute hypoxia is expected to occur in any fractionated treatment (Ljungkvist et al, 2006), and will lead to changes in the radiosensitivity on the microscale. Due to the random nature of these fluctuations, the radiosensitivity distribution throughout the treatment can be characterised by an average radiosensitivity and a corresponding variance. Consequently, Toma-Dasu and co-workers proposed that the boost-dose can be calculated based on an average uniform dose D¯ σ 2 required for a given level of tumour control probability, and a variance D corresponding to the random fluctuations in radiosensitivity (Toma-Dasu, Dasu & Brahme, 2009a; Toma-Dasu et al, 2012). Thus, while the dose distribution

44 resulting from considering the pO2 on voxel level would be highly heterogeneous, the average and the variance of that distribution can be used to calculate the uniform boost-dose required for a given level of TCP, effectively taking into account the inter-fraction ﬂuctuations in radiosensitivity. Thus, the heterogeneity in tumour oxygenation on voxel level is taken into account by calculating the dose Dˆ (r) delivered in n fractions required in each voxel at position r for a given level of tumour control probability P:

α(r) 1 4β(r) Vρ Dˆ (r)=n 1 + ln − 1 (4.1) 2β(r) n α2(r) −lnP where α(r) and β(r) are the LQ parameters modiﬁed according to the OER (equation 2.2, and ρ is the clonogen density in the target volume V. The uniform boost-dose Dboost required in this volumes could then be calculated as:

D¯ D = (4.2) boost γ σ 2 − D 1 2P D¯ where γ is the normalised slope of the dose-response curve corresponding to the pre-defined level of tumour control probability P. This approach could be applied on multiple levels within e.g. the clinical target volume (CTV) , so that different doses could be prescribed to the HTV, the GTV excluding the HTV, and the CTV excluding the GTV. Similarly, several HTVs could be considered, as illustrated in Figure 4.2. The possibility of considering several HTVs while being robust against random fluctuations on voxel level implies an advantage over DPBC. Furthermore, various patterns of hypoxia distribution within tumours have been found in e.g. head-and-neck cancer, where both single hypoxic sub-volumes and more complicated patterns consisting of several areas characterised as hypoxic were observed (Grosu et al, 2007). Such findings demonstrate the need for a versatile yet robust method for the identification and quantification of tumour hypoxia, as well as for the subsequent dose prescription. In this context, robustness against uncertainties associated with inter-fraction variations is also highly relevant with respect to the limitations in resolution offered by PET imaging, as the PET image represents a smoothed version of the underlying microscopic distribution of hypoxia. As in reality, it is this distribution and not the signal intensity that will determine the radioresistance, discrepancies between the voxel intensity and the microenvironment could impact on the outcome of treatments that were

45 designed based on imaging. For small volumes, these discrepancies could become particularly pronounced (Christian et al, 2009; Troost et al, 2008), warranting caution in dose painting approaches targeting small volumes.

Figure 4.2: Illustration of dose-prescription based on imaging and quantifying tumour hypoxia. Several hypoxic target volumes (HTVs) could be identiﬁed, and prescribed doses according their hypoxic radioresistance as quantiﬁed by functional imaging and taking fractionation into account. Similarly, the dose required in the remainder of the GTV (GTV-ΣHTVs) and, correspondingly, the remainder of the CTV (CTV-GTV) can be calculated using the same method.

4.3.3 Dose prescription based on multi-modal functional imaging

While homogeneous escalation to e.g. the entire CTV is inevitably limited by the normal tissue toxicity, a highly heterogeneous dose-prescription is associated with uncertainties that might threaten the outcome of the treatment. If on the other hand, reasonably sized sub-volumes within the target are considered for dose escalation based on the quantification of the radiation resistance within those volumes, a robust dose distribution with higher feasibility and probability of cure could be created. Such sub-volumes could preferably be created based on multi-modal functional imaging, where the hypoxia imaging data could be complemented by PET imaging based on markers for glucose metabolism, such as fluorodeoxyglucose (FDG), and proliferation, such as fluorothymidine (FLT) (Bollineni et al, 2016). By studying the overlap between the uptake of different tracers, the tumour physiology of relevance for radiotherapy could be further characterised and subsequently targeted. For example, tumour sub-volumes with increased uptake of both FDG and a hypoxia- specific tracer could reflect a cell population that is hypoxic and metabolising, but not proliferating, while FDG-avid areas with no hypoxia could reflect a

46 proliferative cell population. Furthermore, by comparing the metabolic and proliferative activity with the hypoxic status, the possibility of differentiating between different forms of hypoxia could be investigated. Such a differentiation in the context of dose prescription could be of great relevance for the doses calculated and hence also the feasibility of delivering the treatment plan (paper II, Lindblom et al, 2017).

4.3.4 The importance of clinical validation of radiobiological modelling In general, radiobiological modelling is associated with large uncertainties. First of all, the application of model parameters derived from in vitro experi- ments for the purpose of describing the outcome of treating an in vivo tumour with radiotherapy can be rightfully questioned. Secondly, the use of relatively simple mathematical expressions to describe the highly complex nature of the biochemical processes governing the eradication of a tumour could almost be considered naïve. However, investigating the impact of individual processes on e.g. the outcome of a treatment necessitates the possibility of creating an otherwise controlled environment where all other processes that also impact on the response of the tumour are constant. In this context, radiobiological modelling constitutes a unique tool capable of uncovering interesting effects that would be impossible to identify in any experimental study. Thus, with the help of modelling, areas worthy of further research including experimental work can be identiﬁed and subsequently pursued. By considering trends in a comparative way as has been described in this thesis, radiobiological modelling can be applied almost independently from the uncertainties in model parameters. As for the simplicity of the expressions used, this can also be considered a pre-requisite in order for a model to be feasibly applied in the clinical routine. A model that is described by a limited number of parameters is also likely to be more robust against inter-patient heterogeneity. Ultimately, the incorporation of modelling in the clinic has to be preceded by model validation against patient outcome. This could in fact be argued to be the only uncertainty of relevance in the context of applying radiobiological modelling in the clinic. Thus, by applying the method described in section 4.3.2 to predict the outcome of patients treated according to an established clinical protocol, and comparing the results with the outcome of a signiﬁcantly large patient cohort, the model can be validated. This will also allow for developing the model and, potentially, revise its parameters, to better describe the clinical reality.

47 48 5. Concluding remarks

Despite the vast amount of scientific literature on the therapeutic issues of tumour hypoxia, little has been done to actively incorporate the oxygenation status of tumours into radiotherapy treatment planning. Thus, a majority of patients receive the same treatment regardless of the oxygenation and thus sensitivity of their individual tumours, which is very likely to affect the outcome of the treatment. Any means of therapeutically targeting hypoxia requires a re- liable method to carefully map and quantify the tumour oxygenation. For this purpose, functional imaging such as positron emission tomography and magnetic resonance imaging techniques play an important role. However, even if functional imaging of hypoxia would be integrated into the treatment planning process, there are no standardised protocols of how to alter the treatment once hypoxia has been detected, i.e. quantifying and relating the level of oxygenation to the dose that should be prescribed accordingly. While the inherent limitations of in silico models to reflect the complexity of clinical cases can be debated, the inter-comparison of simulation results of different treatment scenarios can serve as a good indication of the trend of expectations in actual patients. For example, the impact of fluctuations in acute hypoxia between fractions on the probability of controlling the tumour has been consistently found to be significant throughout this work, in simulations of radiotherapy for a broad range of fractionation schedules and tumour oxygenations. Thus, while direct comparisons between calculated predicted values and the outcome of clinical patient treatments may be disputable, radiobiological modelling offers the tools for testing new approaches without compromising the patient safety. Furthermore, this work has demonstrated how radiobiological modelling in conjunction with functional imaging of tumour hypoxia can be incorporated into the treatment planning process. While the clinical application of the methods described has to be preceded by adequate validation against the outcome of a significantly large cohort of patients, radiobiological modelling has a unique potential of advancing the practice of individualised radiotherapy at an accelerated pace by identifying the most promising research directions. This is strongly believed to result in higher rates of local control and, ultimately, more patients being cured from their cancer.

49 50 6. Summary of papers

Paper I In paper I, two cell survival models were compared for the purpose of modelling the response of heterogeneous populations to radiotherapy. Thus, the performance of the linear-quadratic (LQ) model and the universal survival curve (USC) model was compared in terms of predicting cell survival and tumour control probability (TCP) for a tumour model with heterogeneous oxygenation, taking into account the dose-modifying effect of hypoxia. For a large range of doses extending well beyond what is commonly considered to be relevant to the LQ model, the difference in both survival and TCP as predicted by the two models was negligible. This paper thus demonstrated the impact of considering tumour heterogeneity in radiobiological modelling.

Paper II In addition to the heterogeneity in sensitivity with respect to the oxygen tension, the time during which cells have been deprived of oxygen can imply another impact on the radiosensitivity. In paper II, alternative expressions for cell survival taking into account the difference in sensitivity for chronically and acutely hypoxic cells in addition to the dose-modifying effect of the oxygen tension were applied to a tumour model with heterogeneous oxygenation. By assuming different response characteristics for the different hypoxic species, the tumour control probability was substantially different compared with considering only the modiﬁcation of radiosensitivity based on the oxygen partial pressure. This paper hence demonstrated the importance of differentiating between different types of hypoxia in addition to considering the dose-modifying effect of the oxygen tension.

Paper III Paper III investigated the impact of an extremely limited number of fractions on the outcome of treating hypoxic tumours with radiotherapy. Using a realistic model for tumour oxygenation and a dose distribution relevant to stereotactic body radiotherapy, the total dose required for a given level of control of a hypoxic tumour was found to be constant for a schedule consisting of three or

51 ﬁve fractions. This paper hence demonstrated the importance of allowing for inter-fraction fast reoxygenation of acute hypoxia, as the expected increase in total dose required due to the repair of sub-lethal damages was counteracted by the reoxygenation.

Paper IV In paper IV, the model previously used for assessing the response of heterogeneous and dynamic tumour oxygenation was further developed to include other radiobiological processes of importance in radiotherapy. Thus, the impact of intra-fraction repair for SBRT-like treatments, and accelerated repopulation for conventionally fractionated treatments were considered in addition to hypoxia and reoxygenation. The paper also investigated the impact of heterogeneous fractionation with respect to the dose per fraction. While heterogeneous fractionation could not be conﬁrmed as beneﬁcial in treating hypoxic tumours, a synergistic effect between the reduction of intra-fraction repair and increase in inter-fraction fast reoxygenation when increasing the number of fractions was found for a limited range of fractions in SBRT-like treatments. This paper has thus demonstrated the potential of using radiobiological modelling for the purpose of investigating the impact of isolated processes on the outcome of radiotherapy, and hence identifying interesting directions of further research.

Paper V In paper V, a model for converting FMISO-PET uptake to oxygen partial pressure (pO2) was applied on HX4-PET images of non-small-cell lung cancer (NSCLC). The conversion function was able to identify hypoxic target volumes (HTVs) in good geometrical agreement with the corresponding, conventionally deﬁned volumes that were based on the uptake relative to a well- oxygenated reference region. However, the level of pO2 within the HTVs was substantially higher (30-40 mmHg) than what is normally considered as hypoxic. The promising performance of the function to identify HTVs of similar size, shape and location as the corresponding uptake-based volumes found in this paper however demonstrated the potential use of this function for converting HX4 uptake to pO2.

Paper VI In paper VI, the conversion function previously developed for FMISO and tested for HX4 in paper V was further developed to be able to accurately quantify the level of pO2 in the segmented HTVs. For the patients included in the study, HTVs in good agreement with uptake-based volumes were found for

52 apO2 threshold of 10 mmHg, and uniform boost doses calculated based on achieving 95% tumour control probability were feasible in comparison with other dose painting studies. This paper hence presented an updated conversion function that could be used to segment HTVs in HX4-PET imaging, and for calculating the uniform boost dose necessary based on the calibrated level of pO2.

53 54 Acknowledgements

There are many people to whom I would like to express my deepest gratitude. First and foremost, I would like to thank my supervisor, Dr. Iuliana Toma- Dasu. Thank you for teaching me what it means to be a scientist, and for all the opportunities you have created for me to grow as a researcher as well as a teacher. Thank you for all of your support, for always being available for discussions, and for all your advice and guidance. I have learned so much from you!

I have been fortunate enough to have two co-supervisors who are truly experts in SBRT: Dr. Ingmar Lax for the ﬁrst half of my PhD, and Dr. Peter Wersäll for the second half. It has been a privilege to discuss with you, thank you for all your kindness and for the invaluable clinical input you have provided.

To all of my co-authors, in particular: Dr. Alexandru Dasu, Dr. Ana Ureba and Dr. Laura Antonovic. Thank you for all your encouragement, and for all of the intriguing discussions we had. Ana, thank you for all your optimism and motivation, especially towards the end of this project. Your genuine curiosity and drive truly inspires me!

To my mentor, Dr. Supriya Krishnamurthy, for all your kindness, and for always being available to give me support and discuss with me. In you, I have found a true mentor and friend.

To Dr. Bo Nilsson and Professor Irena Gudowska, for all of your efforts in the medical radiation physics program, and for all of your encouragement throughout the years. Thank you Irena, for giving me the opportunity to take part in and contribute to teaching activities and outreach work, and for always valuing my opinion. I have developed a lot through this work and our discussions, and for that I am truly grateful to you.

During my PhD, I have been fortunate to get to know two very kind and know- ledgeable radiobiologists, Professor Andrzej Wojcik and Dr. Margareta Edgren. Thank you for patiently answering all my questions and engaging in discussions with me on radiobiology, science and life in general. I learned a lot from you! To Helena, my good friend and ofﬁce mate for the past years: I am grateful for all the support I have found in you, for all the discussions we have shared and for all the fun we have had together!

To my good friend Marta, for all of our stimulating discussions on precisely everything, and for your friendship and support. Thank you also for your valuable input on this thesis, which has deﬁnitely made it better and has made me feel more prepared for the defence.

To all my colleagues and friends at MSF throughout the years, in particular all of my fellow juniors with whom I shared countless interesting discussions: Helena, Marta, Ana, Jakob, Oscar, Thomas, Gracinda, Hamza, Laura and Toshiro - I had a lot of fun with you all over the world! I would also like to thank Mariann Eklund and Mona Holgerstrand for all the administrative support over the years.

To my dear friend Marie, who made the medical radiation physics program so much more fun, for all the good times that we had together so far and all the discussions that we shared. Thank you for being such a good friend-Iwould really love for us to become colleagues one day!

To my father, Dr. Peter Lindblom, who introduced me to medical radiation physics in the ﬁrst place, for always believing in me and for being my biggest supporter. Thank you for teaching me about math and physics since I was little, and for inspiring me to study physics. Without all of your love and encouragement, I never would have come this far.

Finally, to my husband Tor, with whom I started my academic career (although neither of us knew it at the time). I am so grateful to have shared not only everyday life with you, but also science, teaching and all other aspects of academia. For all that we have already been through together – it still only feels like the beginning!

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