Ce modèle est formulé pour le normo-fractionnement et l'hypofractionnement (de préférence modéré à fort), en revanche ses hypothèses ne permettent pas de l'utiliser pour les basses doses par fraction.

Des essais cliniques et des méthodes d'imagerie (par TEP ou IRM) pourraient permettre d'améliorer la connaissance des paramètres du modèle et de progresser dans la qualité des fractionnements pour l'immuno-radiothérapie.

Dans un avenir proche, d'autres données cliniques d'immuno-radiothérapie seront disponibles et permettront de mieux cerner la validité du modèle.

Ce modèle n'aborde pas certains points importants (volumes d'irradiation, toxicités auto-immunes, pharmacodynamie spécifique de l'immunothérapie associée...).

Cependant, il représente à notre connaissance le premier modèle pratique de fractionnement adapté à l'immuno-radiothérapie.

Ce type de modèle pourrait permettre de progresser dans la découverte de fractionnements optimaux pour l'immuno-radiothérapie, comme le modèle linéaire-quadratique a servi pour l'exploration de fractionnements alternatifs en radiothérapie classique.
# Immunologically effective dose: a practical model for immuno-radiotherapy

Raphaël Serre<fnref n="1" />, Fabrice Barlesi<fnref n="2" />, Xavier Muracciole<fnref n="3" /> and Dominique Barbolosi<fnref n="1" />

## ABSTRACT

**Objectives:** Concomitant radiotherapy with immune checkpoint blockade could be synergistic. Out-of-field effects could improve survival by slowing or blocking metastatic spreading. However, not much is known about the optimal size per fraction and inter-fraction time in that new context.

**Methods:** The new concept of Immunologically Effective Dose (IED) is proposed: it models an intrinsic immunogenicity of radiotherapy schedules, i.e. the fraction of immunogenicity that results from the choice of the dosing regimen. The IED is defined as the single dose, given in infinitely low dose rate, that produces the same amount of abscopal response as the radiation schedule being considered. The IED uses the classic parameters of the BED formula and adds two parameters for immunogenicity that describe the local availability of immune effectors within the tumor micro-environment. Fundamentally, the IED adds a time dimension in the BED formula and describes an intrinsic immunogenicity level for radiotherapy.

**Results:** The IED is positively related to the intensity of the out-of-field, radiotherapy-mediated, immune effects described in some preclinical data. Examples of numerical simulations are given for various schedules. A web-based calculator is freely available.

**Conclusions:** Out-of-field effects of radiotherapy with immune checkpoint blockers might be better predicted and eventually, radiotherapy schedules with better local and systemic immunogenicity could be proposed.

**Advances in knowledge:** A model for the intrinsic level of immunogenicity of radiotherapy schedules, referred to as the Immunologically Effective Dose (IED), that is independent of the type of immunotherapy.

## INTRODUCTION

The abscopal effect of radiotherapy describes a rarely observed phenomenon in which one or more metastases located out of the irradiation field regress at some point in time after radiotherapy [1]. Described more than sixty years ago, the effect was rare, unpredictable

and for these reasons, considered more like an interesting curiosity than an actual therapeutic target. Today it is largely suspected that a significant part of out-of-field effects could be mediated by the immune system, hence the recent introduction and large diffusion of immune checkpoint blockade has strongly revived interest for the subject, with promising clinical case reports [2], [3] a

<footnote n="1">¹Simulation & Modelling Adaptive Response for Therapeutics in Cancer (SMARTc), Center for Research on Cancer of Marseille, CRCM Inserm UMR1068, CNRS UMR7258, Aix Marseille Université U105, Institut Paoli Calmettes, Marseille, France</footnote>

<footnote n="2">²Multidisciplinary Oncology & Therapeutic Innovations Department, Assistance Publique-Hopitaux Marseille, Marseille, France</footnote>

<footnote n="3">³Department of Radiotherapy, Oncology, CHU La Timone, Assistance Publique-Hopitaux de Marseille, Marseille, France</footnote>

<footnote>**Correspondence to:** Raphaël Serre, email: raphael.serre@inserm.fr</footnote>

<footnote>**Keywords:** immunotherapy; radiotherapy; combination; fractionation; linear-quadratic model</footnote>

<footnote>Received: May 02, 2018</footnote>

<footnote>Accepted: June 19, 2018</footnote>

<footnote>Published: August 07, 2018</footnote>

<footnote>**Copyright:** Serre et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</footnote>
sizeable amount of preclinical results [4] and the exciting on-going development of a new hybrid discipline: immuno-radiotherapy.

The out-of-field mechanism could initially involve an in-field recruitment of dendritic cells (DC) and other types of Antigen Presenting Cells (APC) in the tumour micro-environment, attracted by radiation-induced inflammation and necrosis. However, these immune effectors have a strong radio-sensitivity: lymphocytes are among the most radiosensitive cells within the body (with doses as low as 0.5 Gy already strongly cytotoxic [5]), while DC and APC may survive higher doses but quickly suffer loss of function [6]. Hence, the local availability of immune effectors could be highly impacted by the choice of interfraction time. Meanwhile, the quantity of tumour antigens could be mainly driven by the size of the dose per fraction and the radio sensitivity of tumour cells: higher doses per fraction could release larger quantities of tumour antigens and produce an increased diversity of epitopes. This reasoning, supported by experimental confirmations from the preclinical setup [8] and several clinical reports, has aroused interest in hypo-fractionated schedules for recent clinical trials of radio-immunotherapy [17].

So far, only a few published mathematical models have focused on the equilibrium between the immune-stimulation and the immune-suppression induced by radiation [5], [9]. To our knowledge, no model explores specifically the relationship between fractionation and abscopal effect.

The biological assumptions underlying the IED model are partly like those previsously published in [9] by our team, which covered specific immune checkpoint blockers (such as anti-PD1 and anti-CTLA4); however, the present model focuses on the *intrinsic* part of immunogenicity, i.e. the part coming only from the radiotherapy schedule.

Other contributing factors to the abscopal response certainly exist: type of immunotherapy, relative scheduling, general state of immunosuppression of the patient, tumor size, local state of immunosuppression within the tumor microenvironment, possibly locations of irradiated site and abscopal target [10], p53 somatic mutations [13], etc. These factors should also be considered as part of a larger multivariate analysis, while the independent predictive value of the dosing regimen could be described by the IED.

## RESULTS

The IED model is compared to pre-clinical experiments of out-of-field responses for radiotherapy schedules used in metastatic breast cancer, as described by Dewan & al in [8].

The importance of interfraction interval on the intrinsic immunogenicity of radiotherapy schedules is

shown on a simple theoretical example with two equal doses.

Last, values of intrinsic immunogenicity are computed for three radiotherapy schedules: the classic normofractionated schedule and two so-called "hybrid" schedules.

### IED is related to abscopal response (whereas BED or EQD2 are not)

In [8], Dewan & al. have injected TSA mouse breast carcinoma into syngeneic mice at two different sites. They irradiated one of the site with either 8 Gy x 3 fractions or 6 Gy x 5 fractions once daily, with and without an anti-CTLA4 antibody and they have measured the growth of the (non-irradiated) secondary site.

With combined anti-CTLA4 treatment, the average delays in growth with respect to control were: 6 days for 6 Gy x 5 fractions, 10 days for 8 Gy x 3 fractions. Hence, while these two schedules had almost identical BED and EQD2 (the definition of EQD2 is given below), the average growth delay of the 3 x 8 Gy schedule was 166% of that of the 5 x 6 Gy schedule. Since these two schedules have almost identical EQD2 and BED, the difference in the average out-of-field response, if it is not caused by randomness, can't be explained by classic radiobiology models alone.

Interestingly, the IED Efficacy of 3 x 8 Gy is more *than twice the IED Efficacy of the* 5 x 6 Gy: apparently, it seems strongly associated with the intensity of the out-of-field response. Numerical values are shown in Table 1.

Remark 1: this association persists when using a more radiosensitive tumour with $\alpha = 0.35$ or with varying values of $\alpha / \beta$, or with different values of $T_D$ (2 days, 3 days, 5 days) and $T_{IR}$ (5 days, 7 days, 10 days). This can be verified with the online IED calculator: www.smartcalculators.online/ied.

Remark 2: the definition of EQD2 is standard, i.e. it is the total dose in Gy, that would produce the same cell kill as the schedule of interest, if it was administered in 2 Gy fractions (assuming a linear-quadratic model for cell kill).

### Impact of the interfraction time on IED Efficacy

The interfraction time is a key factor for the local availability of intra-tumour immune effectors; with the IED model, this availability is increased either with a short inter-fraction time ($< T_D$) or with long inter-fraction time ($> T_D + T_{IR}$), but this availability is sharply decreased for intermediary inter-fraction values. This is illustrated on Figure 1 where two equal doses of 2 Gy are given with varying time interval, from 6 hours to 21 days. For the second radiation dose to not kill the immune effectors recruited by the necrotic tumour cells from the first radiation dose, the second radiation dose must be
<table><caption>Table 1: IED efficacy, other dosing metrics and experimental correlates for results in [8].</caption><thead><tr><th>RT schedule</th><th>α</th><th>α / β</th><th>EQD2 (Gy)</th><th>BED (Gy)</th><th>IED efficacy</th><th>out-of-field growth delay (days)</th></tr></thead><tbody><tr><td>3 × 8 Gy (J1-J2-J3)</td><td>0.15</td><td>4</td><td>48</td><td>72</td><td>74%</td><td>10</td></tr><tr><td>5 × 6 Gy (J1 to J5)</td><td>0.15</td><td>4</td><td>50</td><td>75</td><td>32%</td><td>6</td></tr></tbody></table>

administered either very early (before necrosis) or very late (after completion of most of the immune stimulation). This has practical consequences if one wants to design more immunogenic radiotherapy schedules, as shown in the next section.

## IED Efficacy of various radiotherapy schedules

To demonstrate how the IED Efficacy indicator might be used, three schedules are provided: a classic normo-fractionated schedule in 2 Gy per fraction over 5 weeks that serves as a reference, and two new schedules, which are hybrid between normofractionated, split-course, and bi-fractionated (Figure 2). Despite the use of one break (10 and 13 days), the new schedules extend over the same total duration (32 days); they also have similar EQD2 and BED. Hence these new schedules should have similar tumor control probability and identical repopulation risk. The doses per fraction are also either identical (2 Gy) or slightly above (3 Gy) but then, only for a limited period (48h). The use of bi-fractionation is proposed to make up for the time lost during the break, but just for a minimum

period and the last two weeks are administered in normo-fractionated fashion.

The IED model predicts a low intrinsic immunogenicity for the normo-fractionated schedule (IED Efficacy: 2%), and greater values for the two hybrid schedules (28% and 67%). These values can be obtained by using the online calculator at www.smartcalculators.online/ied.

## DISCUSSION

The clear positive relationship between IED Efficacy and the intensity of the out-of-field response observed with the data published in [8] is a strength of the model, but this needs to be confirmed on more experimental data.

The model prediction that normofractionated schedules should have lower immunogenicity than shorter schedules with higher doses per fraction seems coherent with some pre-clinical data [7], [8]. However, since out-of-field effects have also been described with normofractionated schedules [14], [15] more investigation

<figure><img src="image_21.png" /><figcaption>Figure 1: IED efficacy (between 0 and 100%) for two equal doses of 2 Gy, given with varying interfraction time (from 6h to 21 days); dashed line: highly radiosensitive tumour (Surviving Fraction 2 Gy = 0.43); solid line: intermediate radiosensitive tumour (Surviving Fraction 2 Gy = 0.70); the IED efficacy drops markedly if interfraction time T > Time to Death (T > T<sub>D</sub>); the IED efficacy recovers most of its value if the interfraction time T > sum of Time to Death and Time to Immune Response (T > T<sub>D</sub> + T<sub>IR</sub>); the general shape is a steep drop in IED efficacy followed by a shallower recovery; notice how the impact of interfraction time decreases with radio resistance: strongly radio-resistant tumours should be less sensitive to the choice of the interfraction time.</figcaption></figure>
is needed to clarify if this translates into a significant reduction of out-of-field responses.

The impact of interfraction time is consistent with pre-clinical data that have explored the dynamics of immune-infiltration in the tumor micro-environment after irradiation, such as Frey & al. [12]: with a peak of immune infiltration between 4 to 8 days after irradiation, radiation doses administered around that time range could be detrimental to anti-tumour immune response: this is indeed described by the IED model. Along a similar line, Formenti & al. [1] consider that the *local* availability of immune effectors inside the tumor micro-environment should be the main driver of anti-tumour immunity for immuno-radiotherapy, which is an idea at the core of the mechanistic approach of this model.

The IED model uses only two new “time” parameters T<sub>D</sub> and T<sub>IR</sub>, whose signification is clear, since they control the speed of post-radiotherapy necrosis (T<sub>D</sub>) and the speed of immune infiltration (T<sub>IR</sub>) within the TME. Furthermore, thanks to a free access through an online calculator, there is no need to understand the fine details of the formula or to use a mathematical software.

The choice T<sub>D</sub> = 3 days is subject to caution, even though its order of magnitude is in line with some pre-clinical experiments [16], but this needs to be further explored: one could investigate whether T<sub>D</sub> could be also dose- or tumour-dependent or if, and when, T<sub>D</sub> might differ from 3 days by a large amount.

For example, if the death of tumor cell was strongly delayed after irradiation (= very high T<sub>D</sub>), to such an extent that most of tumor death would occur long after the completion of the irradiation schedule, then the timing of radiation doses would not have any impact on immunogenicity (because all integrals θ<sub>ij</sub> would be zero for j < N, and all θ<sub>IN</sub> would be one; hence, the matrix Θ would be constant). It would be interesting to explore the potential circumstances under which this case might occur.

The two examples of new “hybrid” schedules that might be more immunogenic than the classic normofractionated schedule are hypothesis-generating. These examples have been provided to show some potential directions for future research, while on the short term, more model validation is necessary.

Obviously, the differences in immunogenicity reflect the hypothesis on the model parameters, especially on T<sub>D</sub> (and to a lesser extent on T<sub>IR</sub>): as discussed, larger values of T<sub>D</sub> would reduce these differences.

The model proposes several hypotheses that could be experimentally challenged, for example:

* immunogenicity is not (only) about the dose per fraction, since large changes in theoretical immunogenicity could be obtained with identical dose per fraction, by “stacking” doses over shorter periods;

* the role of the interfraction time (which is inexistent in the classic linear-quadratic model) seems important for the local availability of immune effectors; however, this effect could be more important towards the start of the irradiation schedule (when live tumor mass is large) rather than towards its end (when live tumor mass should be much smaller);

* it follows that the total duration of the radiotherapy schedule does not have to be increased to improve immunogenicity, in contrast with the traditional split-course schedules which used to extend the total duration; this property is interesting since it avoids an increase in repopulation risk that could result from treatment extension;

The validity of the IED model for doses per fraction greater than 10 Gy is questionable: first, it relies on a linear-quadratic model whose validity above 10 Gy is not firmly established; second and more importantly, some pre-clinical data ([7], [8]) show that for very large doses per fraction (> 15 Gy), the IED model presented here would vastly

<figure><img src="image_22.png" /><figcaption>**Figure 2:** IED efficacy for three schedules: (A) normofractionated schedule (50 Gy in 25 fractions) very low IED Efficacy = 2%, (B) split-bifrac-1; proposal of new schedule that starts with one short bifractionation (2+2) for one day, split with 10-day break, 9 days of bi-fractionation (2+2) then 2 weeks of normofractionated, with same duration than A, same repopulation risk, EQD2 and BED than A, higher IED Efficacy = 28%; (C) split-bifrac-2 proposal of new schedule, starting with one short bifractionation (3+3) over 2 days, split with 13-day break, one week bifractionation (2+2), two weeks normofractionated, same duration than A, same repopulation risk, slightly higher EQD2 and BED than A, but much higher IED Efficacy = 67%.</figcaption></figure>
overstate immunogenicity. However, an ad-hoc downward adjustment in the IED formula may provide a good correlate with [7], [8], even for > 10 Gy; this optional adjustment is available in the online calculator.

Finally, it is undeniable that predicting abscopal response is a difficult task, since it should depend on many parameters that may be patient-, organ-, tumour- or immunotherapy-specific. The IED model should not be misinterpreted as a tentative to replace this multi-

dimensional problem by the computation of just one indicator. To give an obvious example, the radiotherapist understands that irradiating a 20g-tumour volume should produce more immune stimulation than the irradiation of a 2g-tumour, all other things being equal. A long list of other confounding factors could be made, whose influence is not in the scope of the proposed model, such as the immune status of the underlying tumor micro-environment ("hot" vs "cold" environment), the intrinsic efficacy of the

<figure><img src="image_23.png" /><figcaption>**Figure 3: The 5-step dynamics of IED model.** Simplified graphical description. The actual model relies on a continuous process rather than a discrete step-by-step dynamic. T_D: Time to Death (days) T_IR: Time to Immune Response (days).</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Figure 4: Graphical description of the two functions used in the IED model.** The number g(x) describes the gradual release of tumour antigens at time x, after irradiation at x = 0. The number f(x) describes the gradual immune stimulation, after tumour antigen release at x = 0. Here, x can be understood as a unit-less time variable, that is replaced by the ratio of an elapsed time over T_D (for g), or elapsed time over T_IR (for f). Their definitions are: $f(x) = 1 - (\frac{1}{2})^x$ and $g(x) = f(x) = 2 \ln(2) \cdot x(\frac{1}{2})^x$, but other expressions could be used without changing the behaviour of the model, if the general shape of these curves is like this example.</figcaption></figure>
concomitant immunotherapy, etc. Some of these factors might be added as covariates in the model, especially the state of the tumor micro-environment that could be described with the value of the time parameter T<sub>IR</sub>; colder (*resp. hotter*) environment could be associated with higher (*resp. smaller*) values of T<sub>IR</sub>.

But even in its simplest form with fixed parameters and no covariate, the IED formula provides a quick rule-of-thumb to assess the relevance of fractionation (from the standpoint of immunogenicity) and, on occasion, avoid the choice of under-performing schedules.

# MATERIALS AND METHODS

## Description of IED model

In the model, the radiotherapy-mediated anti-tumour immune response is proportional to the fraction of tumour cells killed by irradiation, whose content has been processed by immune effectors before the next irradiation. Hence, depending on the interfraction time, the number of killed tumour cells that contribute to the immune response may be significantly smaller than the total cell kill predicted by the BED.

More precisely, each radiation dose kills a certain number of cells, with a surviving fraction described by the two parameters (α, β) of a classic linear-quadratic model [11]. Then, lethally wounded cells die in a gradual process and produce tumour antigens in the tumor micro environment.

The time parameter T<sub>D</sub> controls the speed of this antigen release: 50% of the tumour antigens are released after a time T<sub>D</sub> (step 1 and 2 in Figure 3). Then, dendritic cells (DC) and other Antigen Presenting Cells (APC) take on tumour antigens and present them to lymphocytes in the draining lymph nodes. All numerical results were obtained with T<sub>D</sub> = 3 days (in coherence with data in [16], more details in the Discussion), while other T<sub>D</sub> values could obviously be considered.

The time parameter T<sub>IR</sub> (Time to Immune Response) controls the speed of this second process: 50% of completion is attained after a time T<sub>IR</sub>, following tumour antigen release (step 3 and 4 in Figure 1). Finally, the next radiation dose kills (or cause the dysfunction of) all immune effectors in the tumor micro-environment (step 5 in Figure 1). All numerical results here are obtained with T<sub>IR</sub> = 7 days, a reasonable estimate for such an immunologic phenomenon.

In summary, the model describes a succession of a progressive immuno-stimulation by dying tumour cells followed by an instantaneous immuno-suppression caused by the next irradiation.

For a unique dose, under the above assumptions, all killed tumour cells should contribute to the immune response. Hence, by defining IED as a unique dose given

at infinitely low-dose rate, the IED value is obtained by solving the equation where the proportion of tumour cells that would be killed by this virtual dose, which is $1 - e^{-\alpha \text{ IED}}$, equals the proportion of tumour cells that have contributed to the immune response as described above.

The direct solving of this equation yields a unique IED for any radiotherapy schedule, which is only defined by the dosing schedule and the choice of the radio-sensitivity parameters (α, β) and of the parameters that controls the immune response dynamics (T<sub>D</sub>, T<sub>IR</sub>). The IED is interpreted as a measure of the intrinsic contribution of the dosing schedule to immunogenicity.

The value E = $1 - e^{-\alpha \text{ IED}}$ (a positive number < 1) is introduced to reflect better the efficacy of the radiotherapy schedule: 1 means maximal efficacy (100% of tumour volume contributed to immune response) and 0 means no immune efficacy (no volume contributed to immune response). This E value is named IED Efficacy.

## Mathematics of IED model

This section may be skipped by non-mathematicians. Using directly the model is possible by using the free web-based calculator, at www.smartcalculators.online/ied.

With the above assumptions, for N equal radiation doses of D Gy given at times T<sub>1</sub>, T<sub>2</sub>,...,T<sub>N</sub>, the vector E of the quantities of “immunogenic” tumour antigens produced by each dose is equal to:

$$E = S \Theta K$$

where:

* S is an N-by-N diagonal matrix with S<sub>ii</sub> = (1-k<sub>D</sub>)<sup>i</sup>, the fraction of surviving tumour cells available just before the i<sup>th</sup> radiation dose, where k<sub>D</sub> = $1 - e^{-\alpha D - \beta D^2}$, is the killed fraction for dose D;

* Θ is an N by N matrix where: θ<sub>ij</sub> = 0 for j < i, or θ<sub>ij</sub> = ∫<sub>T<sub>j</sub></sub><sub>T<sub>i</sub></sub> g(t-T<sub>i</sub>/T<sub>D</sub>)f(T<sub>j+1</sub>-t/T<sub>IR</sub>)dt for j ≥ i; the functions g and f are detailed in Figure 4;

* K is a N-by-1 vector whose components are equal to k<sub>D</sub>;

For example, with N = 3 doses, E = S Θ K is:

$$ \begin{bmatrix} E_1 \\ E_2 \\ E_3 \end{bmatrix} = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 1-k_D & 0 \\ 0 & 0 & (1-k_D)^2 \end{pmatrix} \begin{bmatrix} \theta_{11} & \theta_{12} & \theta_{13} \\ 0 & \theta_{22} & \theta_{23} \\ 0 & 0 & \theta_{33} \end{bmatrix} \begin{bmatrix} k_D \\ k_D \\ k_D \end{bmatrix} $$

Finally, IED is obtained by solving $E_1 + ... + E_N = 1 - e^{-\alpha \text{IED}}$.

The sum of the components of E is named IED Efficacy; it represents the % of tumor volume that is converted into “immunogenic” antigens, hence it is easier to interpret than the crude IED number.
The $\theta_{ij}$ coefficients are computed numerically (for $j = N$, the upper bound of the integral should be set to $+\infty$).

The integral $\theta_{ij}$ represents the fraction of immunogenic antigens produced by tumour cells killed by the $i^{th}$ radiation at time $T_i$ but released later between the times $T_j$ and $T_{j+1}$; $g(x)$ is the rate of release of tumour antigens per unit of time, after one irradiation (at $x = 0$); $f(x)$ is the completion rate of the immune activation triggered by the release (at $x = 0$) of tumour antigens, hence it is a positive number smaller than one. The parameters $T_D$ and $T_{IR}$ control the speed of each phenomenon.

Hence, the matrix $\Theta$ depends only on the timing of radiation doses and on the time parameters ($T_D$, $T_{IR}$), whereas the vector $K$ and the matrix $S$ depend upon the size of the dose per fraction and the radio-sensitivity parameters $(\alpha, \beta)$.

From $E = S\Theta K$, it is obvious that initial radiation doses contribute to the most part of immunogenic tumour antigens, because of the geometric decrease of the components of $S$.

## CONCLUSIONS

The Immunologically Effective Dose (IED) model complements the classic linear-quadratic formula by considering the dynamics of the immune infiltration within the tumor micro-environment. Then, it produces forecasts about the impacts of various radiotherapy schedules on the anti-tumor immune response.

Understanding the result of the IED model is easy with the IED Efficacy, which is the percentage of tumour volume that gives "immunogenic" tumour antigens: the closest to one, the better.

Obtaining the IED Efficacy of any radiotherapy schedule can be done with an online calculator that is freely available at www.smartcalculators.online/ied. Two parameters are needed: $T_D$ and $T_{IR}$, in addition to the classic parameters $(\alpha, \beta)$ of the linear-quadratic model.

It is not recommended to use the model with doses per fraction above 10 Gy, however, an ad-hoc adjustment parameter may be used if necessary.

With the IED algorithm, new radiotherapy schedules could be investigated in-silico, before being confirmed by preclinical data or clinical trials.

However, the validation of the model on more preclinical or clinical data is the next major step.

## ACKNOWLEDGMENTS AND FUNDING

This work was supported as part of the SMART Project by a grant from the A'Midex foundation (Aix Marseille Univ, Marseille France) and the Investissement d'Avenir national program.

## CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

## REFERENCES

1. Formenti SC, Demaria S. Systemic effects of local radiotherapy. The Lancet Oncology. 2009; 10: 718-26. https://doi.org/10.1016/S1470-2045(09)70082-8.

2. Postow MA, Callahan MK, Barker CA, Yamada Y, Yuan J, Kitano S, Mu Z, Rasalan T, Adamow M, Ritter E, Sedrak C, Jungbluth AA, Chua R, et al. Immunologic correlates of the abscopal effect in a patient with melanoma. The New England Journal of Medicine. 2012; 366: 925-31. https://doi.org/10.1056/NEJMoa1112824.

3. Golden EB, Demaria S, Schiff PB, Chachoua A, Formenti SC. An abscopal response to radiation and ipilimumab in a patient with metastatic non-small cell lung cancer. Cancer Immunology Research. 2013; 1: 365-72. https://doi.org/10.1158/2326-6066.CIR-13-0115.

4. Deloch L, Derer A, Hartmann J, Frey B, Fietkau R, Gaipl US. Modern radiotherapy concepts and the impact of radiation on immune activation. Frontiers in Oncology. 2016; 6: 141. https://doi.org/10.3389/fonc.2016.00141.

5. Yovino S, Kleinberg L, Grossman SA, Narayanan M, Ford E. The etiology of treatment-related lymphopenia in patients with malignant gliomas: modeling radiation dose to circulating lymphocytes explains clinical observations and suggests methods of modifying the impact of radiation on immune cells. Cancer Investigation. 2013; 31: 140-4. https://doi.org/10.3109/07357907.2012.762780.

6. Merrick A, Errington F, Milward K, O'Donnell D, Harrington K, Bateman A, Pandha H, Vile R, Morrison E, Selby P, Melcher A. Immunosuppressive effects of radiation on human dendritic cells: reduced IL-12 production on activation and impairment of naive T-cell priming. British Journal of Cancer. 2005; 92: 1450. http://dx.doi.org/10.1038/sj.bjc.6602518.

7. Schaue D, Ratikan JA, Iwamoto KS, McBride WH. Maximizing tumor immunity with fractionated radiation. International Journal of Radiation Oncology*Biology*Physics. 2012; 83: 1306-10. https://doi.org/10.1016/j.ijrobp.2011.09.049.

8. Dewan MZ, Galloway AE, Kawashima N, Dewyngaert JK, Babb JS, Formenti SC, Demaria S. Fractionated but not single-dose radiotherapy induces an immune-mediated abscopal effect when combined with anti-CTLA-4 antibody. Clinical Cancer Research. 2009; 15: 5379-88. https://doi.org/10.1158/1078-0432.CCR-09-0265.

9. Serre R, Benzekry S, Padovani L, Meille C, Andre N, Ciccolini J, Barlesi F, Muracciole X, Barbolosi D. Mathematical modeling of cancer immunotherapy and its synergy with radiotherapy. Cancer Research. 2016; 76: 4931-40. https://doi.org/10.1158/0008-5472.CAN-15-3567.
10. Poleszczuk JT, Luddy KA, Prokopiou S, Robertson-Tessi M, Moros EG, Fishman M, Djeu JY, Finkelstein SE, Enderling H. Abscalop benefits of localized radiotherapy depend on activated T-cell trafficking and distribution between metastatic lesions. Cancer Research. 2016; 76: 1009–18. https://doi.org/10.1158/0008-5472.CAN-15-1423.

11. Fowler JF. The linear-quadratic formula and progress in fractionated radiotherapy. The British Journal of Radiology. 1989; 62: 679–94. https://doi.org/10.1259/0007-1285-62-740-679.

12. Frey B, Rückert M, Weber J, Mayr X, Derer A, Lotter M, Bert C, Rödel F, Fietkau R, Gaipl US. Hypofractionated Irradiation Has Immune Stimulatory Potential and Induces a Timely Restricted Infiltration of Immune Cells in Colon Cancer Tumors. Frontiers in Immunology. 2017; 8: 231. https://doi.org/10.3389/fimmu.2017.00231.

13. Camphausen K, Moses MA, Ménard C, Sproull M, Beecken WD, Folkman J, O'Reilly MS. Radiation abscopal antitumor effect is mediated through p53. Cancer Res. 2003; 63: 1990–3.

14. Grimaldi AM, Simeone E, Giannarelli D, Muto P, Falivene S, Borzillo V, Giugliano FM, Sandomenico F, Petrillo A, Curvietto M, Esposito A, Paone M, Palla M, et al. Abscalop effects of radiotherapy on advanced melanoma patients who progressed after ipilimumab immunotherapy. Oncoimmunology. 2014; 3: e28780. https://doi.org/10.4161/onci.28780.

15. Chandra RA, Wilhite TJ, Balboni TA, Alexander BM, Spektor A, Ott PA, Ng AK, Hodi FS, Schoenfeld JD. A systematic evaluation of abscopal responses following radiotherapy in patients with metastatic melanoma treated with ipilimumab. Oncoimmunology. 2015; 4: e1046028. https://doi.org/10.1080/2162402X.2015.1046028.

16. Perez CA, Brady LW. Principles and practice of radiation oncology. 2004.

17. Kang J, Demaria S, Formenti S. Current clinical trials testing the combination of immunotherapy with radiotherapy. Journal for ImmunoTherapy of Cancer. 2016; 4. https://doi.org/10.1186/s40425-016-0156-7.
Conclusion

L'actualité des essais cliniques nous rappelle que la recherche de combinaisons efficace est nécessaire pour augmenter les taux de réponse aux immunothérapies: selon les molécules et les tumeurs, les taux de réponse des inhibiteurs de checkpoint en monothérapie oscillent autour de 20%, tandis que certaines combinaisons permettent parfois de doubler ce taux.

La radiothérapie a un rôle particulier à jouer dans ces stratégies de combinaison car elle peut avoir une action ablative, sans les effets immunosuppresseurs des chimiothérapies ni la trop grande sélectivité des thérapies ciblées, tandis que le relargage important des néo antigènes tumoraux pourrait potentialiser la réponse immunitaire anti-tumorale. Si ces notions se diffusent de plus en plus dans la communauté des oncologues-radiothérapeutes et même au-delà, grâce aux travaux pionniers de certaines équipes et à l'engagement personnel de chercheurs influents, à ce stade relativement peu a été accompli pour clarifier les fractionnements et les séquencements optimaux pour l'immuno-radiothérapie. Comme il l'a été rappelé lors de la session de la SFRO du 6-8 octobre 2021, même si des données cliniques encourageantes commencent à être disponibles, les modalités pratiques restent à définir: fractionnement des doses, volumes d'irradiations, séquencement néoadjuvant, séquentiel ou adjuvant...sont des questions toujours non résolues. Les dernières données cliniques tendent à complexifier le tableau: les toxicités de l'immuno-radiothérapie se dévoilent, elles pourraient être parfois limitantes.

Conscients de ces problèmes, nous avons essayé d'apporter une contribution du point de vue théorique en cherchant des modèles mathématiques permettant d'optimiser numériquement des schémas d'immuno-radiothérapie. Vers 2015, nous pensions qu'en l'absence de modélisation mathématique, il serait difficile de progresser sur le long terme. En effet, si la radiobiologie s'est constituée sur des décennies et s'est appuyée (initialement et par nécessité) sur l'empirisme, il fut nécessaire de formuler précisément des concepts (tels les fameux 4-Rs de la radiobiologie) et afin de progresser, d'imaginer des modèles mathématiques, dont certains sont couramment utilisés aujourd'hui, comme le modèle linéaire-quadratique. Ajouter l'immunologie dans la radiobiologie nécessite donc un travail d'intégration de certaines notions d'immunologie dans la modélisation de la radiobiologie, la difficulté étant dans le choix
des éléments nécessaires et suffisants à intégrer. En 2021, si l'approche empirique de l'immuno-radiothérapie a permis de démontrer la faisabilité, d'obtenir des données de toxicité et de montrer des résultats encourageants et parfois mitigés, beaucoup de chemin reste à accomplir et l'immuno-radiothérapie n'a pas encore fait la preuve formelle de son utilité parmi les autres options de combinaison avec l'immunothérapie (que sont les chimiothérapies, les thérapies ciblées, ou les combinaisons de plusieurs immunothérapies). Par ailleurs, il semble que le véritable potentiel de synergie entre la radiothérapie et l'immunothérapie n'a pas encore été complètement libéré par les modalités expérimentées, qui ne surclassent pas les taux de réponse obtenus en combinaison avec d'autres thérapeutiques. Progresser au niveau théorique nous semble un pré-requis pour dépasser ces limites sur le long terme.

Dans ce travail de recherche, nous avons initialement décrit la synergie entre radiothérapie et immunothérapie grâce à un modèle d'inspiration globalement mécanistique, comportant cependant une représentation abstraite et simplifiée (phénoménologique) du système immunitaire. Ce premier modèle est relativement complexe de mise en œuvre, car très paramétré. Si l'on se replace dans le contexte de l'année de sa publication (2016), il n'existait alors aucun modèle sur ce sujet et il était impossible de se baser sur un modèle existant pour le simplifier et en tirer un outil pratique. Il était également difficile de proposer directement un modèle simplifié ne s'intéressant, comme le second modèle, qu'à la dynamique des effecteurs immunitaires en relation avec le fractionnement. De plus, ce premier modèle, grâce à son côté mécanistique, permet une grande flexibilité dans les simulations et génère des hypothèses sur le timing du séquencement entre radiothérapie et immunothérapie ou sur l'intrication entre la taille tumorale et l'efficacité thérapeutique. Ainsi il suggère l'existence d'une fenêtre d'opportunité avec des implications pratiques, comme l'existence d'un nombre minimal de lésions à irradier ou l'hypothèse de l'utilité du debulking pour resensibiliser aux immunothérapies, qui sont actuellement testées dans des essais cliniques. Il insiste également sur l'importance de la concomitance entre radiothérapie et immunothérapie et permet de discuter des compromis à trouver entre la pharmacocinétique des immunothérapies et la cinétique propre de la tumeur. Cependant, comme nombre de modèles complexes et mécanistiques, ces conclusions sont plus volontiers qualitatives que quantitatives: elles ouvrent des pistes à explorer
par des essais cliniques et à analyser par des outils plus simples (comme des statistiques descriptives ou des modèles PK-PD).

Ce premier modèle a permis de tester la qualité du système dynamique tumeur-antigènes-effecteurs, qui s'est montrée utile, en dépit de sa simplicité. Aussi avons-nous conservé ce triptyque pour développer un deuxième modèle visant à explorer spécifiquement la question du fractionnement pour l'immuno-radiothérapie. En raison de la versatilité des immunothérapies, nous avons modélisé une immunogénicité *intrinsèque* à un fractionnement de radiothérapie, c'est-à-dire, sa capacité à produire des néo antigènes tumoraux puis à les faire traiter par le système immunitaire. Nous avons enlevé des équations les particularités liées aux immunothérapies, afin de produire des résultats valables que ce soit pour des checkpoint inhibiteurs, des CAR T cells, ou même des cytokines. La question de quelle immunothérapie à combiner pour une tumeur en particulier est laissée aux spécialistes (immunologistes ou oncologues médicaux) tandis que l'oncologue-radiothérapeute peut se concentrer sur le *meilleur fractionnement possible*.

Cette immunogénicité intrinsèque repose sur la dose totale et la dose par fraction, mais aussi sur le choix des intervalles entre les fractions, qui régulent la disponibilité des effecteurs immunitaires au sein du micro environnement tumoral, par l'intermédiaire de leur cytotoxicité sur l'ensemble des cellules. Ce modèle calcule ainsi une *dose immunologique efficace*, qui peut s'exprimer, au choix, comme une dose ionisante en gray ou comme un pourcentage sans unité, nommé *efficacité immunologique*. A ce stade, on constate une bonne association entre cette *dose immunologique efficace*, ou son équivalent l'*efficacité immunologique*, et le taux de réponse à l'immuno-radiothérapie dans des données expérimentales mais aussi des données cliniques récemment publiées. Cette *dose immunologique efficace* pourrait un jour venir compléter les notions classiques de *dose biologique efficace* (BED) et autres équivalents de dose familiers de la radiothérapie classique. Mais avant celà, des validations sur des données cliniques supplémentaires seront bien sûr nécessaires.

La complexité du modèle de la *dose immunologique efficace* est réduite, puisque seulement deux paramètres de cinétique sont nécessaires, les autres paramètres étant ceux du modèle linéaire quadratique. Le premier paramètre décrit la cinétique de la mort cellulaire tumorale après irradiation tandis que le deuxième décrit la cinétique de l'infiltration immunitaire. On connaît des estimations raisonnables de ces paramètres
dans des conditions expérimentales; il est possible que des méthodes de préférence non invasives (d'imagerie, des biomarqueurs) puissent affiner leur connaissance en clinique.

Qualitativement, ce modèle permet de dégager des règles permettant de construire des fractionnements à la fois *ablatifs* (par leur dose cumulée) et *immunogènes* (par le choix approprié des doses par fraction et des intervalles libres): regroupement de doses en *clusters*, respect d'intervalles inter-fractions suffisamment longs, possibilité de normo fractionner sans impact négatif sur l'immunogénicité au delà d'une certaine dose cumulée.

Nous soutenons qu'il faut essayer de conserver à la radiothérapie son caractère *ablatif*, même en combinaison avec l'immunothérapie, pour plusieurs raisons: l'efficacité de l'immunothérapie peut décroître avec la taille tumorale, certains clones peuvent être immuno-résistants, l'échappement immunitaire n'est qu'un pilier parmi d'autres de la cancérogenèse, et il n'est pas certain qu'une radiothérapie non-ablative présente un intérêt par rapport à des (poly-) chimiothérapies ou des thérapies ciblées. Ces considérations seront bien sûr à adapter en fonction des toxicités auto-immunes: les doses sous-ablatives ou même les basses doses pourraient dans ce cas constituer des options pour des patients bien sélectionnés, à risque élevé de toxicité auto-immune grave.

Quantitativement, malgré une complexité supplémentaire par rapport au modèle linéaire quadratique lié à ses 2 paramètres supplémentaires, le calcul du modèle est simple grâce à un calculateur accessible en ligne.

Si le modèle de la *dose immunologique efficace* semble une solution théorique à la question du fractionnement, d'autres questions importantes ne sont pas encore adressées par la modélisation, comme les toxicités de l'immuno-radiothérapie, le choix des volumes d'irradiation et en particulier la place du traitement prophylactique des aires ganglionnaires (et si l'on dépasse le cadre de la radiothérapie, la place des curages ganglionnaires de stadification).

Un regain d'intérêt pour les basses doses de radiothérapie à visée d'immuno stimulation se manifeste actuellement, à ces niveaux de dose les hypothèses de la dose immunologique efficace, dans sa formulation actuelle, ne sont pas valides et il faudrait adapter le calcul.

Concernant les toxicités de l'immuno-radiothérapie, les données sont encore trop parcellaires et elles devraient être abordées en priorité sous l'angle des histogrammes
doses-volumes: il est encore trop tôt pour juger de la pertinence ou de l'éventuelle obsolescence, de ce formalisme éprouvé.

Au début de ce manuscrit, nous avons rappelé les différents piliers de la cancérogenèse. Si l'échappement immunitaire est l'un d'entre eux, il ne constitue pas le moteur principal de toutes les tumeurs. Il est donc utile de continuer la recherche sur l'immuno-radiothérapie mais aussi sur d'autres combinaisons adressant d'autres piliers, comme par exemple la reprogrammation métabolique tumorale, un autre facteur limitant de la réponse immunitaire, qui pourrait jouer un rôle synergique avec l'immunothérapie et la radiothérapie (145).
Références bibliographiques

1. Jin J-Y, Mereniuk T, Yalamanchali A, Wang W, Machtay M, (Spring)Kong F-M, et al. A framework for modeling radiation induced lymphopenia in radiotherapy. Radiother Oncol. mars 2020;144:105-13.

2. Fowler JF. The linear-quadratic formula and progress in fractionated radiotherapy. Br J Radiol. août 1989;62(740):679-94.

3. Meille C, Barbolosi D, Ciccolini J, Freyer G, Iliadis A. Revisiting Dosing Regimen Using Pharmacokinetic/Pharmacodynamic Mathematical Modeling: Densification and Intensification of Combination Cancer Therapy. Clin Pharmacokinet. août 2016;55(8):1015-25.

4. Hénin E, Meille C, Barbolosi D, You B, Guitton J, Iliadis A, et al. Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients. Breast Cancer Res Treat. avr 2016;156(2):331-41.

5. Meille C, Guerreiro N, Jullion A, Bauer S, Chatterjee M, Vos F de, et al. Abstract CT154: Optimization of the dose and schedule of an HDM2 inhibitor NVP-HDM201 in a first-in-human Phase I study using a mechanism-based PK/PD model. In: Clinical Trials [Internet]. American Association for Cancer Research; 2017 [cité 23 août 2021]. p. CT154-CT154. Disponible sur: http://cancerres.aacrjournals.org/lookup/doi/10.1158/1538-7445.AM2017-CT154

6. Ward MC, Vicini F, Chadha M, Pierce L, Recht A, Hayman J, et al. Radiation Therapy Without Hormone Therapy for Women Age 70 or Above with Low-Risk Early Breast Cancer: A Microsimulation. Int J Radiat Oncol. oct 2019;105(2):296-306.

7. Hannoun-Levi J-M, Chamorey E, Boulahssass R, Polgar C, Strnad V. Endocrine therapy with accelerated Partial breast irradiatiOn or exclusive ultra-accelerated Partial breast irradiation for women aged ≥ 60 years with Early-stage breast cancer (EPOPE): The rationale for a GEC-ESTRO randomized phase III-controlled trial. Clin Transl Radiat Oncol. juill 2021;29:1-8.

8. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. sept 2018;19(9):1180-91.

9. Hartenstein A, Lübbe F, Baur ADJ, Rudolph MM, Furth C, Brenner W, et al. Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone. Sci Rep. déc 2020;10(1):3398.

10. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. cell. 2011;144(5):646-74.

11. ICGC Prostate UK Group, Gundem G, Van Loo P, Kremeyer B, Alexandrov LB, Tubio JMC, et al. The evolutionary history of lethal metastatic prostate cancer. Nature. avr 2015;520(7547):353-7.

12. Ferlito A, Rinaldo A, Devaney KO, Nakashiro K, Hamakawa H. Detection of lymph node micrometastases in patients with squamous carcinoma of the head and neck. Eur Arch Otorhinolaryngol. oct 2008;265(10):1147-53.

13. Zhao P. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. 2019;14.

14. Coussens LM, Werb Z. Inflammation and cancer. Nature. 19 déc 2002;420(6917):860-7.

15. Korniluk A, Koper O, Kemona H, Dymicka-Piekarska V. From inflammation to cancer.
Ir J Med Sci. févr 2017;186(1):57-62.

16. Weber DD, Aminzadeh-Gohari S, Tulipan J, Catalano L, Feichtinger RG, Kofler B. Ketogenic diet in the treatment of cancer – Where do we stand? Mol Metab. mars 2020;33:102-21.

17. Papillon J. Present status of radiation therapy in the conservative management of rectal cancer. Radiother Oncol. avr 1990;17(4):275-83.

18. Dhadda AS, Martin A, Killeen S, Hunter IA. Organ Preservation Using Contact Radiotherapy for Early Rectal Cancer: Outcomes of Patients Treated at a Single Centre in the UK. Clin Oncol. mars 2017;29(3):198-204.

19. Joiner MC, Johns H. Renal Damage in the Mouse: The Response to Very Small Doses per Fraction. Radiat Res. mai 1988;114(2):385.

20. Murray Brunt A, Haviland JS, Wheatley DA, Sydenham MA, Alhasso A, Bloomfield DJ, et al. Hypofractionated breast radiotherapy for 1 week versus 3 weeks (FAST-Forward): 5-year efficacy and late normal tissue effects results from a multicentre, non-inferiority, randomised, phase 3 trial. The Lancet. mai 2020;395(10237):1613-26.

21. Ling CC, Gerweck LE, Zaider M, Yorke E. Dose-rate effects in external beam radiotherapy redux. Radiother Oncol. juin 2010;95(3):261-8.

22. Wang JZ, Mayr NA, Yuh WTC. A Generalized Linear-Quadratic Formula for High-Dose-Rate Brachytherapy and Radiosurgery. Int J Radiat Oncol. nov 2007;69(3):S619-20.

23. Fowler JF. Brief summary of radiobiological principles in fractionated radiotherapy. Semin Radiat Oncol. janv 1992;2(1):16-21.

24. Eschwege F, Bourhis J, Girinski T, Lartigau E, Guichard M, Deblé D, et al. Predictive assays of radiation response in patients with head and neck squamous cell carcinoma: a review of the Institute Gustave Roussy experience. Int J Radiat Oncol Biol Phys. 1 nov 1997;39(4):849-53.

25. Skladowski K, Law MG, Maciejewski B, Gordon Steel G. Planned and unplanned gaps in radiotherapy: the importance of gap position and gap duration. Radiother Oncol. févr 1994;30(2):109-20.

26. Bourhis J, Burtness B, Licitra LF, Nutting C, Schoenfeld JD, Ait Sarkouh R, et al. TrilynX: A phase 3 trial of xevinapant and concurrent chemoradiation for locally advanced head and neck cancer. J Clin Oncol. 20 mai 2021;39(15_suppl):TPS6091-TPS6091.

27. Fuks Z, Kolesnick R. Engaging the vascular component of the tumor response. Cancer Cell. août 2005;8(2):89-91.

28. Lesueur P, Lequesne J, Grellard J-M, Dugué A, Coquan E, Brachet P-E, et al. Phase I/IIa study of concomitant radiotherapy with olaparib and temozolomide in unresectable or partially resectable glioblastoma: OLA-TMZ-RTE-01 trial protocol. BMC Cancer. déc 2019;19(1):198.

29. Peeters STH, Heemsbergen WD, Koper PCM, van Putten WLJ, Slot A, Dielwart MFH, et al. Dose-Response in Radiotherapy for Localized Prostate Cancer: Results of the Dutch Multicenter Randomized Phase III Trial Comparing 68 Gy of Radiotherapy With 78 Gy. J Clin Oncol. 1 mai 2006;24(13):1990-6.

30. Kannan null, Jain null. Oxidative stress and apoptosis. Pathophysiol Off J Int Soc Pathophysiol. sept 2000;7(3):153-63.

31. Ryter SW, Kim HP, Hoetzel A, Park JW, Nakahira K, Wang X, et al. Mechanisms of cell death in oxidative stress. Antioxid Redox Signal. janv 2007;9(1):49-89.

32. Rainaldi G, Ferrante A, Indovina PL, Santini MT. Induction of apoptosis or necrosis by
ionizing radiation is dose-dependent in MG-63 osteosarcoma multicellular
spheroids. Anticancer Res. juin 2003;23(3B):2505-18.

33. Castle KD, Kirsch DG. Establishing the Impact of Vascular Damage on Tumor Response to High-Dose Radiation Therapy. Cancer Res. 15 nov 2019;79(22):5685-92.

34. Inoue H, Tani K. Multimodal immunogenic cancer cell death as a consequence of anticancer cytotoxic treatments. Cell Death Differ. janv 2014;21(1):39-49.

35. Stone HB, Peters LJ, Milas L. Effect of host immune capability on radiocurability and subsequent transplantability of a murine fibrosarcoma. J Natl Cancer Inst. nov 1979;63(5):1229-35.

36. Vaage J. Influence of tumor antigen on maintenance versus depression of tumor-specific immunity. Cancer Res. mars 1973;33(3):493-503.

37. Vaage J, Doroshow JH, DuBois TT. Radiation-induced Changes in Established Tumor Immunity'. 34:10.

38. Oettgen HF. Immunotherapy of cancer. N Engl J Med. 1 sept 1977;297(9):484-91.

39. Magnussen AL, Mills IG. Vascular normalisation as the stepping stone into tumour microenvironment transformation. Br J Cancer. 3 août 2021;125(3):324-36.

40. Mondini M, Nizard M, Tran T, Mauge L, Loi M, Clémenson C, et al. Synergy of Radiotherapy and a Cancer Vaccine for the Treatment of HPV-Associated Head and Neck Cancer. Mol Cancer Ther. juin 2015;14(6):1336-45.

41. Tubiana M. Repopulation in Human Tumors: A Biological Back-Ground for Fractionation in Radiotherapy. Acta Oncol. janv 1988;27(2):83-8.

42. Baumann M, Krause M, Thames H, Trott K, Zips D. Cancer stem cells and radiotherapy. Int J Radiat Biol. janv 2009;85(5):391-402.

43. Emami B, Lyman J, Brown A, Cola L, Goitein M, Munzenrider JE, et al. Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol. mai 1991;21(1):109-22.

44. Mornex F, Pavy JJ, Denekamp J, Bolla M. [Scoring system of late effects of radiations on normal tissues: the SOMA-LENT scale]. Cancer Radiother J Soc Francaise Radiother Oncol. 1997;1(6):622-68.

45. McBride WH, Chiang C-S, Olson JL, Wang C-C, Hong J-H, Pajonk F, et al. A Sense of Danger from Radiation ¹. Radiat Res. juill 2004;162(1):1-19.

46. Dobosz P, Dzieciątkowski T. The Intriguing History of Cancer Immunotherapy. Front Immunol. 17 déc 2019;10:2965.

47. Mehdipour A, Yousefi-Ahmadipour A, Kennedy D, Kazemi Arababadi M. Ionizing radiation and toll like receptors: A systematic review article. Hum Immunol. juin 2021;82(6):446-54.

48. Yasuda K, Takeuchi Y, Hirota K. The pathogenicity of Th17 cells in autoimmune diseases. Semin Immunopathol. mai 2019;41(3):283-97.

49. Olkhanud PB, Damdinsuren B, Bodogai M, Gress RE, Sen R, Wejksza K, et al. Tumor-Evoked Regulatory B Cells Promote Breast Cancer Metastasis by Converting Resting CD4β T Cells to T-Regulatory Cells. Cancer Res. :12.

50. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 23 janv 2020;577(7791):549-55.

51. Göschl L, Scheinecker C, Bonelli M. Treg cells in autoimmunity: from identification to Treg-based therapies. Semin Immunopathol. mai 2019;41(3):301-14.

52. Rowshanravan B, Halliday N, Sansom DM. CTLA-4: a moving target in immunotherapy. Blood. 4 janv 2018;131(1):58-67.

53. Hellmann MD, Paz-Ares L, Bernabe Caro R, Zurawski B, Kim S-W, Carcereny Costa E,
et al. Nivolumab plus Ipilimumab in Advanced Non-Small-Cell Lung Cancer. N Engl J Med. 21 nov 2019;381(21):2020-31.

54. Francisco LM, Sage PT, Sharpe AH. The PD-1 pathway in tolerance and autoimmunity: PD-1 pathway, Tregs, and autoimmune diseases. Immunol Rev. 15 juin 2010;236(1):219-42.

55. Schoenfeld AJ, Antonia SJ, Awad MM, Felip E, Gainor J, Gettinger SN, et al. Clinical definition of acquired resistance to immunotherapy in patients with metastatic non-small cell lung cancer. Ann Oncol. sept 2021;S0923753421044550.

56. König D, Läubli H. Mechanisms of Immune-Related Complications in Cancer Patients Treated with Immune Checkpoint Inhibitors. Pharmacology. 28 juill 2020;1-14.

57. Berner F, Bomze D, Diem S, Ali OH, Fässler M, Ring S, et al. Association of Checkpoint Inhibitor-Induced Toxic Effects With Shared Cancer and Tissue Antigens in Non-Small Cell Lung Cancer. JAMA Oncol. 1 juill 2019;5(7):1043-7.

58. Robert L, Tsoi J, Wang X, Emerson R, Homet B, Chodon T, et al. CTLA4 Blockade Broadens the Peripheral T-Cell Receptor Repertoire. Clin Cancer Res. 1 mai 2014;20(9):2424-32.

59. Mizrahi M, Ilan Y. The gut mucosa as a site for induction of regulatory T-cells. Curr Pharm Des. 2009;15(11):1191-202.

60. Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y, Alegre M-L, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 5 janv 2018;359(6371):104-8.

61. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 5 janv 2018;359(6371):91-7.

62. Wang Y, Wiesnoski DH, Helmink BA, Gopalakrishnan V, Choi K, DuPont HL, et al. Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis. Nat Med. déc 2018;24(12):1804-8.

63. Khan S, Gerber DE. Autoimmunity, checkpoint inhibitor therapy and immune-related adverse events: A review. Semin Cancer Biol. août 2020;64:93-101.

64. Xu C, Chen Y-P, Du X-J, Liu J-Q, Huang C-L, Chen L, et al. Comparative safety of immune checkpoint inhibitors in cancer: systematic review and network meta-analysis. BMJ. 8 nov 2018;363:k4226.

65. Nishino M, Giobbie-Hurder A, Hatabu H, Ramaiya NH, Hodi FS. Incidence of Programmed Cell Death 1 Inhibitor-Related Pneumonitis in Patients With Advanced Cancer: A Systematic Review and Meta-analysis. JAMA Oncol. 1 déc 2016;2(12):1607.

66. Matos I, Martin-Liberal J, García-Ruiz A, Hierro C, Ochoa de Olza M, Viaplana C, et al. Capturing Hyperprogressive Disease with Immune-Checkpoint Inhibitors Using RECIST 1.1 Criteria. Clin Cancer Res. 15 avr 2020;26(8):1846-55.

67. Arasanz H, Zuazo M, Bocanegra A, Chocarro L, Blanco E, Martínez M, et al. Hyperprogressive Disease: Main Features and Key Controversies. Int J Mol Sci. 3 avr 2021;22(7):3736.

68. Ai L, Chen J, Yan H, He Q, Luo P, Xu Z, et al. Research Status and Outlook of PD-1/PD-L1 Inhibitors for Cancer Therapy. Drug Des Devel Ther. sept 2020;Volume 14:3625-49.

69. Cózar B, Greppi M, Carpentier S, Narni-Mancinelli E, Chiossone L, Vivier E. Tumor-Infiltrating Natural Killer Cells. Cancer Discov. janv 2021;11(1):34-44.

70. Khan M, Arooj S, Wang H. NK Cell-Based Immune Checkpoint Inhibition. Front Immunol. 13 févr 2020;11:167.
71. Osipov A, Saung MT, Zheng L, Murphy AG. Small molecule immunomodulation: the tumor microenvironment and overcoming immune escape. J Immunother Cancer. déc 2019;7(1):224.

72. Wagner J, Wickman E, DeRenzo C, Gottschalk S. CAR T Cell Therapy for Solid Tumors: Bright Future or Dark Reality? Mol Ther J Am Soc Gene Ther. 4 nov 2020;28(11):2320-39.

73. Larson RC, Maus MV. Recent advances and discoveries in the mechanisms and functions of CAR T cells. Nat Rev Cancer. mars 2021;21(3):145-61.

74. Durante M, Formenti SC. Radiation-Induced Chromosomal Aberrations and Immunotherapy: Micronuclei, Cytosolic DNA, and Interferon-Production Pathway. Front Oncol. 29 mai 2018;8:192.

75. Formenti SC, Demaria S. Future of Radiation and Immunotherapy. Int J Radiat Oncol. sept 2020;108(1):3-5.

76. Galluzzi L, Vanpouille-Box C, Bakhoum SF, Demaria S. Snapshot: CGAS-STING Signaling. Cell. 22 mars 2018;173(1):276-276.e1.

77. Tsai M-H, Cook JA, Chandramouli GVR, DeGraff W, Yan H, Zhao S, et al. Gene Expression Profiling of Breast, Prostate, and Glioma Cells following Single versus Fractionated Doses of Radiation. Cancer Res. 15 avr 2007;67(8):3845-52.

78. Dewan MZ, Galloway AE, Kawashima N, Dewyngaert JK, Babb JS, Formenti SC, et al. Fractionated but Not Single-Dose Radiotherapy Induces an Immune-Mediated Abscalopal Effect when Combined with Anti-CTLA-4 Antibody. Clin Cancer Res. 1 sept 2009;15(17):5379-88.

79. Vanpouille-Box C, Alard A, Aryankalayil MJ, Sarfraz Y, Diamond JM, Schneider RJ, et al. DNA exonuclease Trex1 regulates radiotherapy-induced tumour immunogenicity. Nat Commun. août 2017;8(1):15618.

80. Dewan MZ, Galloway AE, Kawashima N, Dewyngaert JK, Babb JS, Formenti SC, et al. Fractionated but Not Single-Dose Radiotherapy Induces an Immune-Mediated Abscalopal Effect when Combined with Anti-CTLA-4 Antibody. Clin Cancer Res. 1 sept 2009;15(17):5379-88.

81. Reits EA, Hodge JW, Herberts CA, Groothuis TA, Chakraborty M, K.Wansley E, et al. Radiation modulates the peptide repertoire, enhances MHC class I expression, and induces successful antitumor immunotherapy. J Exp Med. 15 mai 2006;203(5):1259-71.

82. Klug F, Prakash H, Huber PE, Seibel T, Bender N, Halama N, et al. Low-Dose Irradiation Programs Macrophage Differentiation to an iNOS+/M1 Phenotype that Orchestrates Effective T Cell Immunotherapy. Cancer Cell. nov 2013;24(5):589-602.

83. Ungvari Z, Podlutsky A, Sosnowska D, Tucsek Z, Toth P, Deak F, et al. Ionizing Radiation Promotes the Acquisition of a Senescence-Associated Secretory Phenotype and Impairs Angiogenic Capacity in Cerebromicrovascular Endothelial Cells: Role of Increased DNA Damage and Decreased DNA Repair Capacity in Microvascular Radiosensitivity. J Gerontol A Biol Sci Med Sci. 1 déc 2013;68(12):1443-57.

84. Joiner MC, Lambin P, Malaise EP, Robson T, Arrand JE, Skov KA, et al. Hypersensitivity to very-low single radiation doses: Its relationship to the adaptive response and induced radioresistance. Mutat Res Mol Mech Mutagen. nov 1996;358(2):171-83.

85. Farooque A, Mathur R, Verma A, Kaul V, Bhatt AN, Adhikari JS, et al. Low-dose radiation therapy of cancer: role of immune enhancement. Expert Rev Anticancer Ther. mai 2011;11(5):791-802.

86. Patel RR, He K, Barsoumian HB, Chang JY, Tang C, Verma V, et al. High-dose
irradiation in combination with non-ablative low-dose radiation to treat metastatic
disease after progression on immunotherapy: Results of a phase II trial. Radiother
Oncol. sept 2021;162:60-7.

87. Demaria S, Ng B, Devitt ML, Babb JS, Kawashima N, Liebes L, et al. Ionizing radiation inhibition of distant untreated tumors (abscopal effect) is immune mediated. Int J Radiat Oncol. mars 2004;58(3):862-70.

88. Mondini M, Levy A, Meziani L, Milliat F, Deutsch E. Radiotherapy-immunotherapy combinations – perspectives and challenges. Mol Oncol. juill 2020;14(7):1529-37.

89. Golden EB, Chhabra A, Chachoua A, Adams S, Donach M, Fenton-Kerimian M, et al. Local radiotherapy and granulocyte-macrophage colony-stimulating factor to generate abscopal responses in patients with metastatic solid tumours: a proof-of-principle trial. Lancet Oncol. juill 2015;16(7):795-803.

90. Formenti SC, Rudqvist N-P, Golden E, Cooper B, Wennerberg E, Lhuillier C, et al. Radiotherapy induces responses of lung cancer to CTLA-4 blockade. Nat Med. déc 2018;24(12):1845-51.

91. Carretero-González A, Lora D, Ghanem I, Zugazagoitia J, Castellano D, Sepúlveda JM, et al. Analysis of response rate with ANTI PD1/PD-L1 monoclonal antibodies in advanced solid tumors: a meta-analysis of randomized clinical trials. Oncotarget. 2 févr 2018;9(9):8706-15.

92. Theelen WSME, Peulen HMU, Lalezari F, van der Noort V, de Vries JF, Aerts JGJV, et al. Effect of Pembrolizumab After Stereotactic Body Radiotherapy vs Pembrolizumab Alone on Tumor Response in Patients With Advanced Non-Small Cell Lung Cancer: Results of the PEMBRO-RT Phase 2 Randomized Clinical Trial. JAMA Oncol. 1 sept 2019;5(9):1276-82.

93. Welsh J, Menon H, Chen D, Verma V, Tang C, Altan M, et al. Pembrolizumab with or without radiation therapy for metastatic non-small cell lung cancer: a randomized phase I/II trial. J Immunother Cancer. oct 2020;8(2):e001001.

94. Dahmani A, Delisle J-S. TGF-β in T Cell Biology: Implications for Cancer Immunotherapy. Cancers. 11 juin 2018;10(6):E194.

95. Twyman-Saint Victor C, Rech AJ, Maity A, Rengan R, Pauken KE, Stelekati E, et al. Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer. Nature. avr 2015;520(7547):373-7.

96. Kalbasi A, Komar C, Tooker GM, Liu M, Lee JW, Gladney WL, et al. Tumor-Derived CCL2 Mediates Resistance to Radiotherapy in Pancreatic Ductal Adenocarcinoma. Clin Cancer Res. 1 janv 2017;23(1):137-48.

97. Abravan A, Eide HA, Helland Å, Malinen E. Radiotherapy-related lymphopenia in patients with advanced non-small cell lung cancer receiving palliative radiotherapy. Clin Transl Radiat Oncol. mai 2020;22:15-21.

98. Yovino S, Kleinberg L, Grossman SA, Narayanan M, Ford E. The Etiology of Treatment-related Lymphopenia in Patients with Malignant Gliomas: Modeling Radiation Dose to Circulating Lymphocytes Explains Clinical Observations and Suggests Methods of Modifying the Impact of Radiation on Immune Cells. Cancer Invest. 4 févr 2013;31(2):140-4.

99. Pike LRG, Bang A, Mahal BA, Taylor A, Krishnan M, Spektor A, et al. The Impact of Radiation Therapy on Lymphocyte Count and Survival in Metastatic Cancer Patients Receiving PD-1 Immune Checkpoint Inhibitors. Int J Radiat Oncol. janv 2019;103(1):142-51.

100. Hecht M, Eckstein M, Rutzner S, von der Grün J, Illmer T, Klautke G, et al. Primary results of the phase II CheckRad-CD8 trial: First-line treatment of locally advanced
head and neck squamous cell carcinoma (HNSCC) with double checkpoint blockade and radiotherapy dependent on intratumoral CD8+ T-cell infiltration. J Clin Oncol. 20 mai 2021;39(15_suppl):6007-6007.

101. Muroyama Y, Nirschl TR, Kochel CM, Lopez-Bujanda Z, Theodros D, Mao W, et al. Stereotactic Radiotherapy Increases Functionally Suppressive Regulatory T Cells in the Tumor Microenvironment. Cancer Immunol Res. nov 2017;5(11):992-1004.

102. Arina A, Beckett M, Fernandez C, Zheng W, Pitroda S, Chmura SJ, et al. Tumor-reprogrammed resident T cells resist radiation to control tumors. Nat Commun. déc 2019;10(1):3959.

103. Weichselbaum RR. The 46th David A. Karnofsky Memorial Award Lecture: Oligometastasis-From Conception to Treatment. J Clin Oncol Off J Am Soc Clin Oncol. 10 nov 2018;36(32):3240-50.

104. Michot JM, Bigenwald C, Champiat S, Collins M, Carbonnel F, Postel-Vinay S, et al. Immune-related adverse events with immune checkpoint blockade: a comprehensive review. Eur J Cancer Oxf Engl 1990. févr 2016;54:139-48.

105. Stock-Martineau S, Magner K, Jao K, Wheatley-Price P. Challenges of Immunotherapy in Stage IV Non-Small-Cell Lung Cancer. JCO Oncol Pract. août 2021;17(8):465-71.

106. Deutsch E, Chargari C, Galluzzi L, Kroemer G. Optimising efficacy and reducing toxicity of anticancer radioimmunotherapy. Lancet Oncol. août 2019;20(8):e452-63.

107. Meng X-M, Nikolic-Paterson DJ, Lan HY. TGF-β: the master regulator of fibrosis. Nat Rev Nephrol. juin 2016;12(6):325-38.

108. Lavigne J, Suissa A, Verger N, Dos Santos M, Benadjaoud M, Mille-Hamard L, et al. Lung Stereotactic Arc Therapy in Mice: Development of Radiation Pneumopathy and Influence of HIF-1α Endothelial Deletion. Int J Radiat Oncol Biol Phys. 1 juin 2019;104(2):279-90.

109. Schubert P, Rutzner S, Eckstein M, Frey B, Schweizer C, Haderlein M, et al. Prospective Evaluation of All-lesion Versus Single-lesion Radiotherapy in Combination With PD-1/PD-L1 Immune Checkpoint Inhibitors. Front Oncol. 29 oct 2020;10:576643.

110. McBride S, Sherman E, Tsai CJ, Baxi S, Aghalar J, Eng J, et al. Randomized Phase II Trial of Nivolumab With Stereotactic Body Radiotherapy Versus Nivolumab Alone in Metastatic Head and Neck Squamous Cell Carcinoma. J Clin Oncol. 1 janv 2021;39(1):30-7.

111. Ratnayake G, Reinwald S, Shackleton M, Moore M, Voskoboynik M, Ruben J, et al. Stereotactic Radiation Therapy Combined With Immunotherapy Against Metastatic Melanoma: Long-Term Results of a Phase 1 Clinical Trial. Int J Radiat Oncol. sept 2020;108(1):150-6.

112. Altorki NK, McGraw TE, Borczuk AC, Saxena A, Port JL, Stiles BM, et al. Neoadjuvant durvalumab with or without stereotactic body radiotherapy in patients with early-stage non-small-cell lung cancer: a single-centre, randomised phase 2 trial. Lancet Oncol. juin 2021;22(6):824-35.

113. Tian S, Switchenko JM, Buchwald ZS, Patel PR, Shelton JW, Kahn SE, et al. Lung Stereotactic Body Radiation Therapy and Concurrent Immunotherapy: A Multicenter Safety and Toxicity Analysis. Int J Radiat Oncol. sept 2020;108(1):304-13.

114. Shaverdian N, Lisberg AE, Bornazyan K, Veruttipong D, Goldman JW, Formenti SC, et al. Previous radiotherapy and the clinical activity and toxicity of pembrolizumab in the treatment of non-small-cell lung cancer: a secondary analysis of the KEYNOTE-001 phase 1 trial. Lancet Oncol. juill 2017;18(7):895-903.
115. Antonia SJ, Villegas A, Daniel D, Vicente D, Murakami S, Hui R, et al. Overall Survival with Durvalumab after Chemoradiotherapy in Stage III NSCLC. N Engl J Med. 13 déc 2018;379(24):2342-50.

116. Peters S, Felip E, Dafni U, Belka C, Guckenberger M, Irigoyen A, et al. Safety evaluation of nivolumab added concurrently to radiotherapy in a standard first line chemo-radiotherapy regimen in stage III non-small cell lung cancer—The ETOP NICOLAS trial. Lung Cancer. juill 2019;133:83-7.

117. Verma V, Simone C, Werner-Wasik M. Acute and Late Toxicities of Concurrent Chemoradiotherapy for Locally-Advanced Non-Small Cell Lung Cancer. Cancers. 8 sept 2017;9(12):120.

118. Eisbruch A, Kim HM, Feng FY, Lyden TH, Haxer MJ, Feng M, et al. Chemo-IMRT of Oropharyngeal Cancer Aiming to Reduce Dysphagia: Swallowing Organs Late Complication Probabilities and Dosimetric Correlates. Int J Radiat Oncol. nov 2011;81(3):e93-9.

119. Antonia SJ, Villegas A, Daniel D, Vicente D, Murakami S, Hui R, et al. Durvalumab after Chemoradiotherapy in Stage III Non-Small-Cell Lung Cancer. N Engl J Med. 16 nov 2017;377(20):1919-29.

120. Sebag-Montefiore D, Stephens RJ, Steele R, Monson J, Grieve R, Khanna S, et al. Preoperative radiotherapy versus selective postoperative chemoradiotherapy in patients with rectal cancer (MRC CR07 and NCIC-CTG C016): a multicentre, randomised trial. The Lancet. mars 2009;373(9666):811-20.

121. Palma DA, Olson R, Harrow S, Gaede S, Louie AV, Haasbeek C, et al. Stereotactic ablative radiotherapy versus standard of care palliative treatment in patients with oligometastatic cancers (SABR-COMET): a randomised, phase 2, open-label trial. The Lancet. mai 2019;393(10185):2051-8.

122. Gomez DR, Blumenschein GR, Lee JJ, Hernandez M, Ye R, Camidge DR, et al. Local consolidative therapy versus maintenance therapy or observation for patients with oligometastatic non-small-cell lung cancer without progression after first-line systemic therapy: a multicentre, randomised, controlled, phase 2 study. Lancet Oncol. déc 2016;17(12):1672-82.

123. Parker CC, James ND, Brawley CD, Clarke NW, Hoyle AP, Ali A, et al. Radiotherapy to the primary tumour for newly diagnosed, metastatic prostate cancer (STAMPEDE): a randomised controlled phase 3 trial. The Lancet. déc 2018;392(10162):2353-66.

124. Kavi A, Gurewitz J, Benjamin CG, Silverman JS, Bernstein K, Mureb M, et al. Hippocampal sparing in patients receiving radiosurgery for ≥25 brain metastases. Radiother Oncol. août 2021;161:65-71.

125. Le Rhun E, Guckenberger M, Smits M, Dummer R, Bachelot T, Sahm F, et al. EANO-ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up of patients with brain metastasis from solid tumours. Ann Oncol. août 2021;S0923753421022146.

126. Huang AC, Postow MA, Orlowski RJ, Mick R, Bengsch B, Manne S, et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature. mai 2017;545(7652):60-5.

127. Qu C, Ping N, Kang L, Liu H, Qin S, Wu Q, et al. Radiation Priming Chimeric Antigen Receptor T-Cell Therapy in Relapsed/Refractory Diffuse Large B-Cell Lymphoma With High Tumor Burden. J Immunother. janv 2020;43(1):32-7.

128. Rini BI, Plimack ER, Stus V, Gafanov R, Hawkins R, Nosov D, et al. Pembrolizumab plus Axitinib versus Sunitinib for Advanced Renal-Cell Carcinoma. N Engl J Med. 21
mars 2019;380(12):1116-27.

129. Motzer R, Alekseev B, Rha S-Y, Porta C, Eto M, Powles T, et al. Lenvatinib plus Pembrolizumab or Everolimus for Advanced Renal Cell Carcinoma. N Engl J Med. 8 avr 2021;384(14):1289-300.

130. Kroeze SGC, Fritz C, Hoyer M, Lo SS, Ricardi U, Sahgal A, et al. Toxicity of concurrent stereotactic radiotherapy and targeted therapy or immunotherapy: A systematic review. Cancer Treat Rev. févr 2017;53:25-37.

131. Shukla N, Hanna N, Durm G. Optimal Duration of Maintenance Checkpoint Inhibitor Therapy in Patients With Advanced NSCLC. JCO Oncol Pract. août 2021;17(8):472-4.

132. Waterhouse DM, Garon EB, Chandler J, McCleod M, Hussein M, Jotte R, et al. Continuous Versus 1-Year Fixed-Duration Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: CheckMate 153. J Clin Oncol. 20 nov 2020;38(33):3863-73.

133. Ahamadi M, Freshwater T, Prohn M, Li C, de Alwis D, de Greef R, et al. Model-Based Characterization of the Pharmacokinetics of Pembrolizumab: A Humanized Anti-PD-1 Monoclonal Antibody in Advanced Solid Tumors: Pharmacokinetics of Pembro in Solid Tumors. CPT Pharmacomet Syst Pharmacol. janv 2017;6(1):49-57.

134. Van Limbergen EJ, Hoeben A, Lieverse RIY, Houben R, Overhof C, Postma A, et al. Toxicity of L19-Interleukin 2 Combined with Stereotactic Body Radiation Therapy: A Phase 1 Study. Int J Radiat Oncol. avr 2021;109(5):1421-30.

135. Schaue D, Ratikan JA, Iwamoto KS, McBride WH. Maximizing Tumor Immunity With Fractionated Radiation. Int J Radiat Oncol. juill 2012;83(4):1306-10.

136. Serre R, Barlesi F, Muracciole X, Barbolosi D. Immunologically effective dose: a practical model for immuno-radiotherapy. Oncotarget. 7 août 2018;9(61):31812-9.

137. Kosinsky Y, Dovedi SJ, Peskov K, Voronova V, Chu L, Tomkinson H, et al. Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model. J Immunother Cancer. déc 2018;6(1):17.

138. Frey B, Rückert M, Weber J, Mayr X, Derer A, Lotter M, et al. Hypofractionated Irradiation Has Immune Stimulatory Potential and Induces a Timely Restricted Infiltration of Immune Cells in Colon Cancer Tumors. Front Immunol [Internet]. 8 mars 2017 [cité 28 sept 2021];8. Disponible sur: http://journal.frontiersin.org/article/10.3389/fimmu.2017.00231/full

139. Grayson JM, Harrington LE, Lanier JG, Wherry EJ, Ahmed R. Differential sensitivity of naive and memory CD8+ T cells to apoptosis in vivo. J Immunol Baltim Md 1950. 1 oct 2002;169(7):3760-70.

140. Wildgruber M, Eisenblätter M. Molecular Imaging of Immunity and Inflammation and Its Impact on Precision Medicine. Biomedicines. 11 janv 2021;9(1):62.

141. An Y-S, Kim S-H, Roh TH, Park SH, Kim T-G, Kim J-H. Correlation Between 18F-FDG Uptake and Immune Cell Infiltration in Metastatic Brain Lesions. Front Oncol. 24 juin 2021;11:618705.

142. Barbolosi D, Hapdey S, Battini S, Faivre C, Mancini J, Pacak K, et al. Determination of the unmetabolised 18F-FDG fraction by using an extension of simplified kinetic analysis method: clinical evaluation in paragangliomas. Med Biol Eng Comput. janv 2016;54(1):103-11.

143. Antonios JP, Soto H, Everson RG, Moughon DL, Wang AC, Orpilla J, et al. Detection of immune responses after immunotherapy in glioblastoma using PET and MRI. Proc Natl Acad Sci. 19 sept 2017;114(38):10220-5.
144. Ahern E, Solomon BJ, Hui R, Pavlakis N, O'Byrne K, Hughes BGM. Neoadjuvant immunotherapy for non-small cell lung cancer: right drugs, right patient, right time? *J Immunother Cancer*. juin 2021;9(6):e002248.

145. Honjo T. Serendipities of Acquired Immunity. *TRENDS Sci. 1 sept 2019;24(9):9_80-9_97.*

146. Murthy V, Maitre P, Kannan S, Panigrahi G, Krishnatry R, Bakshi G, et al. Prostate-Only Versus Whole-Pelvic Radiation Therapy in High-Risk and Very High-Risk Prostate Cancer (POP-RT): Outcomes From Phase III Randomized Controlled Trial. *J Clin Oncol. 10 avr 2021;39(11):1234-42.*