Introduction

Radiotherapy continues to hold a significant place in the treatment of cancer worldwide, with one estimate suggesting that as many as 4 in 10 cancer patients will receive radiotherapy as part of their treatment [1], [2]. Furthermore, radiotherapy is cost-efficient typically accounting for only a fraction of the total cost of treatment [3] with ongoing technological advances dramatically increasing the precision and efficacy of radiotherapy tools [4]. Radiotherapy is commonly applied [5]–[8] as a ‘multi-fraction’ program worked out by the radiotherapy planner consisting of small (1-5 Gy) doses applied with a fixed inter-fraction regime (e.g. once or twice daily) with weekends normally given over to rest. Multi-fraction (low-dose) programs are regarded for their ability to deliver larger total radiation doses without the associated negative impacts on the surrounding healthy tissue that would be associated with an equivalent single-dose protocol. However, if one considers a program (or protocol) as a list of time-gaps between fractions where the time-gaps could take any value between 18 h to 30 h in 30 min steps, then even a two-week, once-per-day, ‘10 fraction’ program, could be constructed in any one of over 95 trillion ways . Against this enormous space, clinicians presently utilise a vanishingly small set of ‘standard’ protocols [5]–[8] with the majority utilising 24 h solar and work-place cycles rather than respecting any in-built metabolic or cellular rhythms of the cancerous cells they target.

An obvious question thus arises: could significant therapeutic gains be identified by simply changing the timing of multi-fraction programs? Ideally a positive answer to this question would not demand large changes to the existing fractional dose or inter-fraction timing already in use in the clinic so as to stay close to the known impacts of radiotherapy on healthy tissues. Clearly, traditional, in vitro (not to mention in vivo) techniques face prohibitively high search costs for the kind of search space available [9]. Indeed, a parallel, combinatorial concern, has recently been voiced by workers in combined drug oncology [10] who argued (p.1) that a ‘qualitatively new approach’ to searching the vast space of possible treatment protocols was required.

In this work, we extend our existing, mature, fully calibrated MCS cellular automata model of EMT6/Ro dynamics [11], [12] by adding a calibrated multi-fraction irradiation module. The extended model is then situated within a powerful genetic algorithm (GA) non-linear, constrained, search environment [13],[14] to discover novel candidate protocols that exhibit superior tumour control properties compared to existing benchmarks. We then conduct a targeted, systematic, search in the region identified by the GA, which yields quasi-optimal, candidates that confer significant and substantial improvements in tumour control over presently used, clinical treatment protocols. Significantly, these improvements arise due only to exploiting a single dimension of freedom: the timing of the fractions. Subsequent numerical simulation demonstrates the robustness of these findings to a variety of small, but feasible, mistakes in the fractionated timing. Finally, we demonstrate the likely mechanism of the efficacy of the quasi-optimal tumours: the exploitation of underlying cell-line dose-response cell-phase dynamics.

Our work differs substantially from the related work of [15] and [16]. First, we develop a high-fidelity model of EMT6/Ro dynamics under multi-dose irradiation as opposed to a low fidelity theoretical exploration. Second, by exploiting the computational power of the GA search technology, our findings result from search as opposed to selective candidate choice. Third, we vary only the timing of the fractions, holding all other characteristics to their clinically active (benchmark) levels so as to minimise the likely introduction of any undesirable side-effects beyond those already associated with the benchmark treatments as opposed to varying both dose and timing, or to adding chemo-therapy agents to the mix.

Hence, our constrained search, on a fully calibrated, high-fidelity, in silico (tumour dynamics/multi-dose irradiation) system, provides considerable hope for translational outcomes in in vitro and ultimately, in vivo environments.

Results

The Calibrated Multi-Fraction Irradiation Module

The multi-fraction irradiation module added to our existing [11], [12] MCS cellular automata model of EMT6/Ro dynamics had a single control parameter, the half-time of reciprocal repair [17] (for details of the module and calibration, see Materials & Methods section). Our calibration strategy for sought to minimise the mean-squared error (MSE) between the effective dose achieved within our extended model as compared to 18 distinct multi-fraction irradiation experiments for EMT6/Ro spheriods as reported in [18] and [19]. The experimental studies provided a robust test of our calibration as together they comprised protocols with fractional-counts ranging from 2 to 5 fractions, and fractional-doses ranging from 4 to 13 Gy.

It was found that the MSE surface was smooth and single peaked, producing an optimal calibration , falling within Fowler's [17] experimentally reported values for the half-time of reciprocal repair, for other cell types. In Fig. 1 we present a comparison of our simulation mean effective dose results at the optimal value of to those of the two reference studies. As can be seen in the figure, the mean simulation results fall within the confidence interval for 17 of the 18 experiments, with the single outstanding experiment falling just outside the interval. We are not aware of any other numerical spheroidal model which achieves such close quantitative fit to a key, summary, measure of multi-fraction irradiation application.

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Figure 1. Comparison of calibrated numerical simulation outputs to equivalent experimental data across 18 independent conditions.

Simulation data (red open symbols) represents mean effective dose for the model at the optimal fit (minimal MSE) value of , averaged over 10 case library tumours at one random seed (10 replicates). Experimental data taken from [18] and [19] (bars): Otsuka et al. 2011, [18], (12 experiments) perform experiments in which the number of fractions and the fractional dose are varied, whilst keeping inter-fraction time-gaps at 4 h; while Sugie et al., [19], (6 experiments) use a consistent 210 Gy protocol, varying the inter-fraction time gap as shown. The grey bars indicate the region of for the experimental data, while the red lines represent the same for the numerical simulations.

https://doi.org/10.1371/journal.pone.0114098.g001

Search by Genetic Algorithm

In all numerical experiments, candidate protocols were scored by comparing the resultant tumour cell count across 10 distinct cases at day 10 (5 days after the end of the treatment period) to the same when one of two benchmark protocols were applied. The 5 day lag after cessation of the treatment period prior to measurement ensures that any re-population phenomena [20] is accounted for in the scoring of candidates. Both benchmarks were constructed to proxy current clinical practice for low-dose, multi-fraction, irraditation treatments [5]–[7]. The characteristics of benchmark I (BMI) and II (BMII) are presented in Table 1. We scored each considered protocol by calculating a one-tailed probability that the candidate produced a significantly improved tumour cell count reduction than the relevant benchmark.

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Table 1. Irradiation Treatment Protocol BMI and BMII.

https://doi.org/10.1371/journal.pone.0114098.t001

In the first phase of candidate search a Genetic Algorithm (GA) [13], [14] was used to efficiently identify search zones within the vast solution space. A total of 1,113 and 1,100 candidates were analysed in benchmark I and II, respectively, comprising over 500 generations. Table 2 presents the results of a detailed study (20 random seeds over all 10 case library tumours: 200 replicates per protocol) of the top 3 protocols in each benchmark region as discovered by the GA methodology. Within the region of BMI, the top ranked candidate led to an average cell-count reduction of 9.4% (range: 3.9–16.5%). For BMII, the top ranked protocol yielded a 7.1% reduction in cell count on average (range: 2.5–13.3%). In terms of significance, each top performing protocol yieled mean p-value comparisons with the relevant benchmark population of 0.039 and 0.069, respectively, satisfying a one-tailed significance test of an improvement in both cases.

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Table 2. Top 3 Performing Protocols Discovered by GA Search.

https://doi.org/10.1371/journal.pone.0114098.t002

Synchronicity

Upon inspection of the resultant library of candidates created and tested through the GA search, it became apparent that there was a tendency for the more successful protocols to exhibit a smaller distribution of inter-fraction time-gaps. That is, as the GA search progressed, the pool of novel candidates appeared to exhibit quasi-synchronicity around a given . This tendency is evidenced by the top ranked candidates presented in Table 2, where of the 33 time-gaps presented, 23 (70%) of them fall within the range 15 h to 20 h.

To explore this phenomenon, density plots of pooled inter-fraction time-gaps from the top 100 candidates (ranked by , where denotes the ith irradiation treatment protocol, see Materials & Methods section) for each benchmark region were plotted (see Fig. 2). From these data, it was found that for both BMI and BMII half of the time-gaps fell in the region 14 h to 19 h (47.4% and 55.6%, respectively for each benchmark) with the modal time-gap in each benchmark being 18.5 h and 15.5 h, respectively.

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Figure 2. Observed inter-fraction gap synchronicity in more successful candidate protocols discovered by GA search.

Density plots of the inter-fraction time-gap (h) for the pooled time-gaps present in the top 100 protocols (ranked by ) for BMI (top) and BMII (bottom). Note: the inter-gap fractions are aggregated into bins of size 1 h.

https://doi.org/10.1371/journal.pone.0114098.g002

Next, following the indications of these results, targeted, periodic time-gap candidate experiments were conducted. Specifically, hand-crafted candidates having periodic time-gaps within the range h and h were applied to the tumour case library as before for benchmark I and II, respectively. To provide as rigorous a test as possible, 10, 20 or 40 distinct random seeds were used for each of the 10 tumours in the case library. Note, the upper bound for BMI must not exceed 17 h since beyond this point, the total protocol treatment period would be greater than 5 days, with over-flowing fractions being dropped (disallowed) by the irradiation module.

Table 3 and Fig. 3 present the results of these experiments. As expected, in each benchmark, the top performing periodic candidate fell within the high-density band indicated by the GA search phase. For BMI, we consider that the top performing, periodic, candidate was the 17 h version. Whilst strictly speaking the 17 h protocol did not demonstrate the best mean performance it nonetheless yielded the strongest overall performance with a significantly higher level of minimum outcome and statistical significance than other protocols with an average normalised cell fraction improvement of 9.4% (range: 5.3–12.3%), at average (one-tailed). Compared to the top-performing protocol from the GA search (refer Table 2) the periodic 17 h candidate yielded a similar benefit, but with higher statistical significance (rank 1 GA BMI candidate ). For BMII, the best performing candidate was found to be the periodic 18 h protocol which produced an average benefit of 5.6% (range: −0.5–12.0%) at an average of 0.181. Whilst not reaching the same level of statistical significance, the average and range of improvement of the periodic 18 h protocol was found to be similar to that of the best GA protocol in BMII.

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Figure 3. Summary performance improvements of periodic candidates tested in each benchmark condition.

Grey bars indicate the range (min – max) of average normalised cell count difference to the given benchmark protocol across the 10 tumours in . Markers indicate the average. Quasi-optimal periodic candidates indicated by arrows. Note that a varying number of random seeds (from 10 to 40) were employed at each periodicity level. See Table 3 for details.

https://doi.org/10.1371/journal.pone.0114098.g003

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Table 3. Performance of periodic candidates under each BM context.

https://doi.org/10.1371/journal.pone.0114098.t003

Discussion

The main aim of this work was to explore numerically the combinatorially vast multi-fraction treatment protocol space which exists around clinically active multi-fraction irradiation protocols. Specifically, it was hoped that significant and substantial tumour control performance gains could be gleaned by making only small, but important, changes to existing protocols, thereby giving any performance enhancing protocols so discovered the best chance of being applicable in the clinic – the ultimate performance theatre. Indeed, if it could be shown that significant and worthwhile benefits could be obtained in a high-fidelity numerical simulation model within a heavily constrained search zone, we believed this would constitute strong evidence that search within a wider, though potentially less comprehensible, search zone (e.g. timing and dose) was well warranted.

To accomplish our aim, to our existing, calibrated, model of EMT6/Ro spheroid growth and single-dose irradiation response [11], [12], we have added and re-calibrated a multi-dose (multi-fraction) irradiation response module. After compiling a 10-tumour case study library, two benchmark (both with total does of 10 Gy, and treatment duration of 5 days, or 1 week) multi-fraction protocols were applied multiple times to each tumour in the case library to establish a baseline performance. The extended, multi-fraction, model was then placed within a GA framework to conduct a coarse-grained, constrained, search for performance improving protocols.

It was found that a convergent feature of the top performing protocols discovered by the GA was temporal periodicity, i.e. the inter-fraction time-gaps converged, in the best protocols, to a small fraction of the available time-gap space. Probing further, we identified a set of quasi-optimal protocols which each comprised constant inter-fraction time-gaps of approx. 17–18 h and resulted in significant ( and , respectively) average tumour size reductions of 9.4% (range 5.3%–12.3%) and 5.6% (range −0.5%–12.0%) over the two benchmark protocols, respectively.

Furthermore, these gains were achieved whilst holding a great number of other characteristics of the protocols at the same levels as the clinically applied, benchmark protocols: the total dose over a week-long period; the number of fractions administered; and the quantum of each fractional dose. The only degree of freedom we exploit is the timing of the fractions within a restricted 5-day treatment period. Further simulations showed that tumour control performance improvements do not depend in a knife-edge way on the exact periodicity of the protocols: aggregate movements away from exact periodicity of up to 4 h over a week (e.g. administering a fraction earlier or later than prescribed by 0-1 h, four times during a week) result in an average reduction in improvement of less than 1.1%.

Whilst the performance gains identified by the present study may appear modest (up to 12% in each benchmark depending on the tumour), it should be remembered that our study considers only a relatively short, first week, of treatment. Furthermore, our methodology allowed for a five-day (no irradiation) re-growth period prior to performance measurement, meaning that performance gains are most likely to be material, lasting, improvements (rather than, for example, due to some particular timing of the last fraction or other confounding effect). Typically, a clinical treatment program will deliver 30–50 Gy [5] at the fractionated timetable considered in our work, i.e. a total treatment period three to five times longer than that which we have considered here. Whilst further, long-run, simulations would be needed to verify any conclusions, it seems reasonable to expect that the cumulative benefit of the quasi-optimal protocols discovered in our study, if applied over a three or five week period, could be considerably more than 10%, possibly in the region of 20% to 40%.

In summary, the principal result of this paper is that robust, significant, and substantial performance improvements likely lie in the near vicinity of existing clinically active multi-fraction irradiation protocols. Granted, following the prescription of this study, patients may have to receive several fractions out-of-hours during the treatment cycle, but, importantly, the protocols likely introduce no new or significantly harmful side-effects on normal tissues. To our initial question of asking if significant gains are likely possible within a highly constrained search zone on a high-fidelity numerical model, we conclude in the affirmative; giving considerable hope for even greater clinical benefits if one relaxes one or more further degrees of freedom in protocol construction.

Placing these results in the existing computational literature is not easy: whilst there has been many advances in the delivery technology of radiotherapy [3], [4], [21] similar advances in treatment protocols appear to have received less attention. The obvious reason for this imbalance is the far greater costs of trialling many treatment protocols under in vivo or in in vitro conditions, and, the high level of complexity in developing multi-scaled, high-fidelity, computational models of tumour dynamics under multi-fraction irradiation. The recent computational models of Kempf et al. [15] and Powathil et al. [16] are perhaps the closest we are aware of to our work, however, neither provides a quantitative calibration of their model so quantitative comparisons of results are not possible. Similarly, neither present results of search for quasi-optimal (or optimal) protocols, instead, presenting the results of given, hand-crafted alternatives to existing clinical protocols. Nevertheless, both works conclude that their tentative results encourage further, more detailed, consideration of irradiation timing especially as it relates to cell-phase cycle considerations. On the other hand, the studies we are aware of which conduct constrained numerical optimisation over irradiation and/or irradiation/chemotherapy solutions [22], [23], find the potential for significant tumour control performance improvements over existing clinical approaches. For instance, Wein et al.'s study [22], which holds fractional timing constant and varies the size of each fraction finds an improvement of around 0.20 in tumour control probability (TCP) for comparable (10 Gy/week, 1 or 2 fraction per day) protocols.

Whilst the results of our work cohere with the small, existing, computational literature on the benefits of cell-phase synchronicity in cancer treatments [15], [16], it must be stressed that our approach differs substantially from previous authors in the level of fidelity our model achieves, and thus, the degree of confidence that might be carried from periodic candidates identified in this work to further experiments in vitro or in vivo. For example, Kempf et al. [15], [24] estimate potential radio-sensitivity ‘windows’ due to cell-cycle synchronisation resulting from multi-fraction irradiation protocols. However, whilst importing some parameters from the experimental EMT6/Ro literature, validation of the mechanisms of the basic (no irradiation) model proceeds only at the dynamics of the total-cell count level, with no validation undertaken of the tumour's other properties (e.g. volume, necrotic core dimensions, cell metabolism, cell-phase dynamics), let alone the tumour's response to single- or multi- dose irradiation. At a higher level of abstraction, Powathil et al. [16] construct a multi-scale system capable of studying combined chemo-/radiation- protocols within a computational, mathematical model. By way of validation, the authors present a comparison between model cell-phase distributions after a single dose (3 Gy) of irradiation with those of an experimental study. However, no quantitative comparison is made, nor are the model outputs compared to multi-dose irradiation experiments, a limitation the authors note (p.10). Similarly, the work of Alfonso et al. [25] which although not considering synchronicity directly, presents a related multi-scale computational spheroid model to study the potential for spatially heterogeneous radiotherapy delivery, includes no external validation. Moreover, in each of these related studies, and many like them [7], [22], [26]–[29], cellular irradiation sensitivity is modeled by employing the Linear-Quadratic (LQ) parameters taken directly from experimental studies; an approach which we have shown in an earlier work [12] can dramatically under-estimate the true probability of cell death due to irradiation, after cell-repair is taken into account.

In contrast, our previous [11], [12] and present works have relied strongly on calibration to a variety of experimental evidence for a single cell line. Not only is our work calibrated to bulk direct- and calculated- (fitted) tumour properties such as cell count, volume, saturation cell count and saturation volume, but also to internal dynamic properties such as the onset of necrosis, the thickness of the viable cell rim, and the progression of the cell-phases over time. Furthermore, we have taken care to rigorously test our model of irradiation response under single- [12] and multi- dose (this work) irradiation. Since the focus of the present work is on the dynamic, and complex, interaction between (at least) cell-metabolism, cell-phase cycling, mitosis, irradiation damage and death, and cellular repair, we contend that without multi-dimensional calibration and validation at each milestone in a model's development, any subsequent results can be considered tentative at best. Subsequently, the results of this paper should be read as complimentary to the foregoing in silico and theoretical works on multi-fraction irradiation optimisation, adding considerable weight to previous indications that cellular synchronicity provides a potentially low cost, yet powerful, tool to enhance clinical outcomes.

As to the mechanism of efficacy of the 17–18 h periodic protocols discovered by this work, we offer only speculative indications. Clearly, one would suspect that, in line with previous contributions [15], [16], [23] the mechanistic locus would lie in the interaction between the timing of the irradiation fractions and the cell-phase dynamics of the developing tumour. In Fig. 4 we present the mean cell-phase dynamics over the 10 tumours of the tumour case library during, and after, the application of the 17 h or 18 h periodic protocol within each of the benchmark contexts, respectively. Unfortunately, it is impossible to validate these traces with experimental data. To the best of our knowledge cell-phase dynamics in response to multi-dose irradiation are available only for V79 Hamster cells [30] under 1.5, 3 or 6 Gy over 12 h of response, and for single-dose irradiation of HeLa cells (3 Gy) [16], or EMT6/Ro cells [31] under 3 or 6 Gy. Clearly, given that different cell lines have substantially different cell-phase periods, it is not possible to compare our model with other cell line results. Nevertheless, our model provides quantitatively similar dynamics when a single dose of 6 Gy is applied as in the EMT6/Ro study mentioned (see File S1), so we draw some confidence that the cell-phase response dynamics of the simulations provides a reasonable indication of reality. Figure 4 demonstrates that in both benchmark contexts, the irradiation fractions create and then coincide with significant periodicity in both the and cell-phase fraction time-series. A direct comparison of the cell-phase dynamics of Fig. 4 and the cell-phase dynamics of the two benchmark treatments (see Fig. S5 in File S1) reveals that the coincident periodicity of the 17–18 h protocols are potentially the key distinguishing factor which confers its performance benefit; the benchmark protocols routinely ‘miss’ both the troughs and cell-phase peaks. Indeed, calculations (not shown) based on the trough dynamics of the post-irradiation region in Fig. 4 gives rise to a mean period of 18.3 h in both the BMI and BMII contexts, whilst the periodic is 22.0 h and 20.3 h, respectively. Since these calculations come from the out-of-treatment region, one could conclude that these measurements reflect the underlying (latent) cell-phase periodicity of the EMT6/Ro cell line. The obvious suggestion of these measurements is that by focussing attention on the 17–18 h periodic protocol region, the natural ‘mode’ that the GA Search and periodic numerical simulations have uncovered, is that of the periodicity. These data suggest that the potentially significant and substantial tumour control benefit which appears available to the patient through optimised dose-timing could be obtained with a basic understanding of the cell-phase dynamics of the cancerous cell-line in question. A possible pathway for clinical application would be to biopsy the tumour mass of focus, stabilise in a series of suitable nutrient matrices, apply a basic coarse-grained multi-fraction protocol to the mass (e.g. as BMII) and then study the induced cell fraction peak post-irradiation (as in Fig. 4 or Fig. S5 of File S1) to suggest a likely periodic protocol target. However, such an approach is speculative only until the latent cycle peak conjecture can be established across multiple cell lines.

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Figure 4. Dynamic cell-phase response of the model under periodic (17 h or 18 h), multi-dose irradiation.

1.25 Gy (BMI) context (top); and 2.00 Gy (BMII) context (bottom). Irradiation symbols and grey lines indicate timing of irradiation fractions. Each cell phase line represents the hourly mean of the cell dynamics traces across each of the 10 tumours in the tumour case library and over 10 unique random seeds (100 replicates in all).

https://doi.org/10.1371/journal.pone.0114098.g004

Whilst the present study has attempted to produce a high-fidelity model of EMT6/Ro MCS growth and dynamics under multi-fraction irradiation, several limitations should be acknowledged. First, despite the fact that one of the key drivers in heterogeneous radiotherapy technology advance is the maximisation of radiation delivered to the tumour mass whilst limiting the impact on healthy tissues [3], [21], such considerations are not implemented here. To this we make a few comments: a) that, in principle, adding heterogeneous irradiation beam types and spatial delivery to our model, would be relatively straight-forward, but calibration of this module without published, high-granularity data, seems (at present) highly unlikely; and b) that one of the chief reasons for reducing the scope of search merely to the timing of the fractions in a narrow, relatively coarse granularity, band, was to minimise the distance between the side-effects of any discovered candidate protocol and the in-use protocol it departed from. Nevertheless, without experimental data arising from an in vivo trial of the periodic protocols proposed in this paper, it is impossible to conclude that negative side-effects on healthy tissues would not arise. Indeed, of the small number of clincial, randomised, trials we are aware of which have considered minor variations to the fractionation schedule [32], [33], adverse (though apparently reversible) morbidity and localised toxicity have been observed in some cases, justifying an approach of cautious optimism.

Second, the periodic candidate protocols arising from the present study cannot be considered anything but ‘quasi’-optimal since in fact, only a vanishingly small fraction of the candidate protocol space was searched by the GA phase of the search methodology. With sufficient compute access or code optimisation, one could either run the GA for many more generations, or conduct a more ‘brute force’ study of the landscape, perhaps sampling at a systematic rate of one candidate per thousand or hundred thousand variants. We will leave such work for other authors to consider. Here, we simply note that GAs are well suited to focussing attention on powerful solution sub-spaces (if not optimal) in a variety of difficult and vast solution landscapes [2], [13], [14], [34]–[36].

Third, what the present study gains in fidelity and quantifiability by virtue of its strong adhearance to experimental data available for a single cell line (EMT6/Ro) it lacks in potential generalisability of its results. To take the inferences of this study further, one would need to apply similar calibration techniques to a second, or third cell line and re-discover any tendency towards periodicity in quasi-optimal protocols as found here. At present, we, like all related authors in this research area, are aware of only the excellent data available for the EMT6/Ro cell line across basic tumour dynamics (including progression of the cell phases over time) [37], [38], single-dose irradiation [39], [40], and multi-fraction programs [18], [19]. According to our knowledge no experimental data on any other cell line appears to come close to the EMT6/Ro library at present. However, we expect that with the declining costs of both in vitro and in vivo laboratory trials, and the gathering momentum of in silico multi-scale computational oncology models, this situation will likely change in the near future.

Finally, a limitation which stands for all studies built on non-human models of cancer is the general animal—human translation problem [41]. This is a long-standing problem, which was considered specifically for the EMT6/Ro cell line as far back as Rockwell in 1980 [42]. As indicated above, the present study's translational efficacy is likely strongly tied to the interaction between the underlying cell-cycle (and phasing) characteristics of the cell line in question and the fractionated dose timing. With adequate characterisation of the former by standard methods, we believe that clinical translation is reasonably likely, especially given the relatively minor deviation our study would suggest away from standard multi-fractionated programs widely used in the clinic.

We conclude by noting several areas of potential for the present line of work, and the development of the model which supports it. First, we have exploited only one degree of freedom in our constrained search of protocol space, one could, for instance, follow [22] and conduct a parallel study which seeks to find a (quasi-)optimal allocation of a total dosage over multi-fractions, holding the inter-fraction time-gap constant. Or, one could allow both the size and timing of the fractions to vary; or one could introduce a chemo-therapy module (as in [16]) to study combined chemo- irradiation- therapy protocols. Obviously, apart from the fixed time-gap, variable fraction study, all other lines of inquiry increase again the size of the already substantial protocol-space, meaning on the one hand that search may take longer, but on the other, that the probability of finding an even greater performance benefit over existing benchmarks arguably increases. Second, and not insignificantly, our work considers pre-vasculature tumours as noted earlier. This is both a beneficial simplification and constraint on our work. A fruitful line of model development would be to add a vasculature development module coupled to a requisite tumour–tissue interaction module, leading to the ability to study larger (vascularturised) tumours, and equivalently the impact of any protocol on healthy tissue. Applying a cost-function for healthy tissue damage during the search phase of our approach would no doubt improve the clinical acceptability of any candidate protocol which departed significantly from currently used clinical benchmarks. Naturally, any extension along these lines would rely heavily on additional experimental data on the cell-line in question to provide critical validation and calibration points in the model's further development. As it stands, this study has already come close to the frontier of such data. No doubt the experimental community will mobilise around this call as computational models grow in sophistication, fidelity, and predictive and explanatory power.

Materials and Methods

The MCS Growth Model

The present work builds on our previously reported EMT6/Ro [11], [12] mouse breast cancer cell line multi-cellular spheroidal (MCS), fully scaled, quasi-2D, CA simulation model. The model has been developed with the consistent aim of achieving high, multi-dimensional, fidelity when compared to published, experimental EMT6/Ro studies. Prior to the present work, the model has passed several developmental milestones including bulk, saturation and internal tumour dynamics without irradiation [11], and the model's dynamic response to single-dose irradiation [12].

In [11] and [12] the basic spheroidal growth model was described and analysed hence we provide here a summary only. For convenience, we provide further details of the underlying model in the File S1. The model includes several key environmental and developmental components which serve to mimic the in vitro experimental conditions most commonly found in the literature and the characteristic biological features of MCSs.

The tumour cells exist within a 2-dimensional lattice whose boundaries are constantly replenished with glucose and oxygen nutrients at 5.5 mM and 0.28 mM, respectively [37], [38]. For computational efficiency and proliferative accuracy a many-to-one assumption is made by placing multiple (N) cells within each lattice site location. Given the cell packing density for EMT6/Ro cells (cell.cm⁻³, [37]) a lattice site side-length can be calculated. Thus, the choice of is the key scaling parameter of the model. As in our previous work, we set in the present study.

Diffusion of substrate nutrients (glucose and oxygen) and toxic metabolic by-products is achieved with a modified discrete diffusion algorithm (see [43] for details) to ensure an isotropic diffusion frontier [44]. The tumour mass is assumed to grow within a well mixed and constantly replenished media, hence, we assume constant substrate-concentration (Dirichlet) boundary conditions for glucose, oxygen and waste products (protons). At each update of the model (equal to 6 s) the diffusion module is run 24 times to ensure stability and accuracy as described in [12]. Diffusion coefficients for each of the three diffused quantities are set as described and explained in [12].

Cell metabolism and cycling are replicated from [12]. Cells (sites) determine metabolism based on a multi-step decision algorithm which incorporates literature defined thresholds for known cell switching between proliferative and quiescent metabolisms in either aerobic or anaerobic modes for EMT6/Ro cells [27], [37], [39], [45]–[52]. (refer Fig.1(a) in [12]) When born, each filled site is endowed with cell cycle duration times, for each of four cycle phases () drawn from known normal distributions [53] for EMT6/Ro [27], summing to the sites total cell cycle duration, (Fig. 5). By default, each site is assumed to preference the proliferative (aerobic, if possible, or anaerobic, otherwise) metabolism mode, and so, progress around their cell cycle towards mitosis (refer Fig.1(b) in [12]). At each model update, the site cell cycle state impacts directly on the outcome of the cellular metabolism module since the latter depends on the former for biological reasons. For example, sites which have reached the (by definition) proliferative or phases cannot access either the aerobic or anaerobic form of the quiescent metabolism. Failure to obtain enough nutrients for proliferation in these modes will enroll such a site into the site-death module. Both the cell cycle and metabolism algorithm modules are fully described in [12].

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Figure 5. Representation of the cell cycle progression.

Cells can be in one of the proliferative (green regions) cell cycle phases: , , or . Each site is born with a unique combination of cell cycle phase timing durations (, where ). During the progression through the cell cycle the site cell cycle progression register is updated by however only when the site can maintain a proliferative metabolism. It is assumed that due to the lack of nutrients or to low pH concentration cells can be forced to switch to the quiescence metabolism (red regions) pausing the progression through the cell cycle. Such a switch is assumed only possible in the or phases. Whenever a site enters the – division phase (which from the biological point of view is a part of the phase) and cannot manage to complete the division, the site death program is initiated.

https://doi.org/10.1371/journal.pone.0114098.g005

Sites which reach the final, division phase of their cell cycle have a fixed amount of time to finalise division (D phase, a sub-phase of the phase). Successful mitosis requires that a vacant site exists in the Moore (8-neighbour) neighbourhood of the parent in the lattice. Daughter sites are endowed with new draws for their cell cycle parameters from the distributions previously described. Both the parent and daughter cell cycle register is set to zero for subseqent progression around their respective cell cycles. The site death module leaves a vacant site and produces a one-off, toxic, necrotic material (see Table S5 of File S1).

Model timing is retained from [12] incorporating the updated two modules (irradiation and repair, described below): 1) Substrate replenishment; 2) Diffusion of all nutrients and toxic substances; 3) Irradiation of the tumour (if applicable); 4) Site metabolism decisions; 5) Nutrient metabolism; 6) Cell phase decisions; 7) Site repair or death after irradiation (if applicable); and 8) Cell division.

All parameters used in the present study are consistent with those reported in Table S5 of File S1. The in silico cultivated MCSs dynamic agrees with the tumour bulk properties experimentally measured and published in [37]–[39], [54], [55] including: the tumour growth rate based on the tumour volume and the diameter changes in time, the saturation cell count, the onset of necrosis, the thickness of the viable rim, the estimated doubling time, the saturation volume and the cell cycle distribution of cells within the tumour as a function of the tumour diameter, for details see [12].

Search

Protocols, Protocol Space, & Benchmark Protocols.

One can consider an irradiation treatment protocol as a vector of (dose, delay) pairs ,(1)where a given th pair defines the irradiation dose to apply at the present fraction (, Gy) together with the wait-time between the previous fraction and the current one (, h). For example, a common irradiation protocol is to apply 2 Gy fractions each day of the working week (Monday–Friday). The first week of such a treatment program could be thus written,

(2)The size of the total protocol space , is then given by,(3)

Obviously the granularity of and will impact non-trivially on . In this study, we focus on search near to clinically tested irradiation treatment protocols arguing that such localised search is likely to produce candidate protocols within clinically acceptable boundaries. Hence, we narrow our search to the vicinity of two particular protocols which mimic standard multi-fraction protocols in use in the clinic [5]–[7], and at that, we vary only the time-delay components () of these protocols, holding both the fractional dose and the total dose administered over the entire protocol constant (i.e. for all experiments). In Table 1 we give details of the two benchmark locations we shall be considering in the study (BMI and BMII), for each, we consider the first five days of a treatment protocol which may last several weeks.

To keep the size of reasonable and to ensure that potential candidates are most likely to be clinically feasible we search in 30 min steps within a 22 h interval around the benchmark (see section Constraints for details).

The Case Library.

Our aim is to discover protocols with robust performance over a diversity of tumours, avoiding over-fitting to a potentially pathological case. To accomplish this aim, a 10 tumour, 10 day, case library, was developed in an identical manner to our approach in [12]: an initiating seed population of 200 cancer cells (10 sites) was placed in-silico in a well-mixed, constantly replenished substrate environment at 5.5 mM and 0.28 mM concentration of glucose and oxygen respectively (with pH maintained at 7.4 as in [12] and grown for 10 days before each tumour state was saved, and stored in for later retrieval. Characteristics of are given in the File S1.

Objective Score Definition.

The objective of this work aligns with that of Engelhart's review of optimal chemotherapy treatment, to, ‘minimise the tumour size’ [23] (p.124). Specifically, we focus on the number of tumour cells existing at the end of each trial relative to the initial number of cells i.e. the normalized cell count. Our approach follows that of [16] in the context of the effectiveness of combined cancer therapies, or [64] which considers the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy, or [15] and [25] who study the effects of different radiotherapy schemes on tumour spheroids. Clearly, the volume of the spheroid could also be used (as is clinically common for practical reasons), however, tumour volume can hide significant changes which may have taken place within the tumour mass (e.g. increased necrotic radius) and so is a less informative summary measure of performance. Moreover, in case of the intense therapy it might happen that we observe the partial de- fragmentation of the tumour and then the measure of the radius of the tumour might be not relevant. Additionally, with in silico methods, number of cells is as computationally inexpensive as volume to calculate.

Prior to evaluating the candidate tumours in the rest of the study, the benchmark protocols were first evaluated, twenty times (20 random seeds), on each member of . These data were then used as a baseline comparison to which the candidates could be compared by the fitness measure described below. Full details of the benchmark protocol performance on can be found in File S1.

Define by to be the normalised cell count for protocol , the ratio of the final cell count (averaged over the last hours of experiment to account for any noise) to the initial cell count. Next, define by,(4)the fitness score of protocol applied to tumour , compared to benchmark BM (where ). For example, a value of of 10.5 indicates that protocol on tumour returned a 10.5% smaller cell-count at the end of the treatment period, relative to the benchmark protocol under study when applied to tumour .

Next, since we conclude that a protocol which outperforms the benchmark treatment a majority of the time, but performs substantially worse than the benchmark some of the time, does not represent a viable candidate for clinical application, we bias in the following way. In any situation where , we apply the transformation to the fitness score, effectively implying that when aggregating over all tumours in , the protocol will suffer a severe fitness penalty.

The above approach yields a sample population of values, for some candidate protocol , one for each 10 members of , representing the expected performance of the given protocol on a diversity of case tumours. To rank each protocol, we finally construct a summary measure of the performance of the candidate by testing the hypothesis that the mean of the sample population is significantly greater than 0. That is, we construct, ; and alternatively , where and denotes the mean and standard deviation of the normal distribution values respectively, and calculate . Ranking by implies that for two candidates and , will be higher ranked than if .

Macro Search Technology: the Genetic Algorithm.

The present search problem is well suited to discrete (or ‘modular’), parallel search methods: each solution comprises an ordered set of delay indices which point to the vector of feasible delay values. In such circumstances, continuous search methods are not feasible [65]. Moreover, since it is possible that small variations in the time-delay between given fractions may have a significant impact on the efficacy of a protocol (e.g. the issue of ‘missed days’ in treatment [66]), the solution landscape likely is non-linear in profile, again, challenging traditional search methods. However, genetic algorithms [13], [14] (GAs) are ideally suited to handling these solution contexts as they are naturally implemented with chromosome-like solution strings and handle well non-linear, modular, contributions to a solution's overall fitness.

GAs have been successfully applied across a vast number of scientific domains, with applications in radiotherapy beam-angle optimisation (for an overview see [2] or for examples [67]–[72]), radiotherapy patient flow planning [34], and applications in other areas of computational biology [35], [36] amongst them. However, to our knowledge, we are the first to apply GAs to the fractional dose program.

Figure 6 provides an overview of the GA approach. Each generation, a GA is applied to the growing library of (fitness, treatment protocol) pairs to produce a population of new protocol candidates for fitness testing. Prior to entering the main loop for the first time, the current candidate protocol population is initialised by random seeding. Subsequently, each generation proceeds in three phases: candidate testing; library addition; and new population formation:

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Figure 6. Representation of the Genetic Algorithm (GA) Search module.

After initialisation, in each generation of the GA all candidates of the current population are run through the calibrated MCS and multi-dose irradiation model against all 10 case-library tumours, afterwhich the fitness (resultant normalised cell count relative to the given benchmark protocol) is calculated for each candidate before being added as a fitness–protocol pair to the GA Library. The new generation starts with the evolution of a new population by the GA module based on the full (updated) GA library. In the study over 1,100 candidates are evolved and tested in each benchmark. For a more detailed description of the whole procedure see File S1.

https://doi.org/10.1371/journal.pone.0114098.g006

1. Candidate Testing. During the testing phase, each candidate protocol in a generation is applied to each of the 10 tumours of the tumour library generating a vector of normalised cell counts which are used to compute an overall fitness score (4) for the candidate;

2. Library addition. Once all candidates in the current population have been tested, the population is added to the candidate library as (fitness pairs—candidate).

3. New population creation. The generation concludes when a new candidate protocol population is created via biased fitness selection and inheritence operators working on the entire (historical) protocol library. Practically, this means selecting one or two better performing protocols, ranked by fitness, from the library, and creating from them a novel candidate for testing by applying standard GA operators.

In broad terms, a GA takes a given ‘fitness’-ranked (highest objective score to lowest) population of solutions and uses these to produce a subsequent population of unique candidates via biased inheritance; the new solutions more likely possessing qualities found in the better performing solutions of the previous population (or ‘generation’). Figure 6 provides an overview of the GA employed in the present work. An initial population of 10 candidate protocols was created using random draws from the protocol-space. Full details of the GA including a description of the inheritance operators used are included in the File S1.

Constraints.

The application of constraints to any search activity is critical: whilst the GA method could discover a given protocol which confers significant relative benefits, it will not be useful if the protocol leads to strongly negative side-effects (e.g. excessive normal tissue damage). For this reason, we apply constraints to our search with the central aim that any candidate protocol studied will depart only marginally from that already used in the clinic, reasoning that such a protocol will not only potentially confer therapeutic benefits but also retain a high probability of generating small, or at least, well-understood, side-effects for normal tissues.

First, since total dose delivered has material impacts on both the tumour and the healthy tissues [73], [74], we follow [34], [75] and constrain the total dose of any protocol to be 10 Gy in both benchmark environments (as per the benchmarks). Second, we apply lower, upper and granularity constraints to the inter-fraction time-gaps. Namely, for search in the vicinity of BMI and BMII, respectively, we define h and h. In each case, a 22 h time-gap interval is available, at 30 min steps. The interval is shifted lower in the case of BMI so that we can search the time-gap region below the smallest BMI time-gap of 6 h, whilst keeping the minimal time-gap distance appreciably greater than 0 so that we retain the focus on low dose, fractionated radiotherapy. Additionally, each chosen interval ensures that search goes beyond both solar (24 h) and cellular ( 20 h for EMT6/Ro cells) cycles. The granularity choice of a 30 min step size is arrived at after considering: a) what could be clinically feasible (e.g. a protocol which relies on the fraction being delivered in a 5 min window is unlikely to be practical); b) the resultant size of the search space; and c) a small enough step size that cell phase cycle or metabolic cycles may come into play. Finally, in each benchmark study, the fractional dose is held constant at 1.25 Gy or 2.0 Gy respectively. Consequently, application of (3) implies search spaces for the two benchmarks of and (: We have total dose 10 Gy, in 1.25 Gy fractions, implying at most 8 fractions; additionally, ; hence, to compute ).

To re-iterate, candidate protocols discovered by the GA will be identical to the benchmark in fractional-dose level, total-dose, and total treatment time; the only flexibility will be in the time-intervals between each fraction, within the given sets.

Supporting Information

Acknowledgments

The authors acknowledge the support of technical staff at the Multi-modal Australian ScienceS Imaging and Visualisation Environment facility (MASSIVE) (http://www.massive.org.au/). Three anonymous reviewers provided helpful comments on an original version of this manuscript which have enhanced the quality of the work.