Jul 16, 2009 ... An overview of classical radiobiology. 5 or 6 R's of radiobiology and their impacts
on treatments. R. Impact/exploitable effect. 1. Repair.
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An overview of classical radiobiology group 4
5 or 6 R’s of radiobiology and their impacts on treatments
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Fractionation – usually hyper fx
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Repopulation
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Hyperbaric treatments, hypoxic sensitisers
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Reassortment
Interfraction interval
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Radiosensitivity
Treatment individualisation ??
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Remote cell kill
Bystander cell killing ?
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Classical radiobiology – an overview John D Fenwick
Radiobiology and the cell kill paradigm
1. Repair: Radiation cell killing
• Radiation damage to tumors and normal tissues classically viewed as being caused by radiation cell death.
• Radiation-induced cell death has been studied for well over 50 years, both in-vivo and in-vitro.
• Quite close to the truth for tumors and early reacting (fast-turnover) tissues such as skin, oral mucosa, and intestinal lining. • Not really true for slowly (or not at all) turning-over tissues like lung, heart, brain, bone etc, but nevertheless provides a useful initial conceptual framework for modelling radiation effects – especially fractionation.
Radiation cell killing • Radiation cell death has most often been determined in-vitro, by plating out equal numbers of irradiated and unirradiated cells on two Petri dishes, and seeing how many cell colonies grow on each plate.
Colonies of cells growing in a flask – from Barber et al 2001
• Results from large numbers of experiments tell broadly the same story, and can be characterised using some simple equations. • This being a study of biological rather than physical systems, while the equations convey a broad truth they are subject to quite a lot of caveats, and so the orthodox radiobiological model which I’ll outline does not describe every situation and every experiment perfectly.
Radiation cell killing • Cell survival is often characterised as the ratio of the number of colonies containing > 50 cells derived from irradiated cells to the number derived from unirradiated cells. • Why 50 or more cells? Because cell colonies sometimes take a while to die out – irradiated cells may be fatally damaged but might still be able to divide a few times before dying. So 50 is an operational figure – if cell division can proceed to the point of producing 50 cells, the cell is considered to have survived.
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Radiation cell killing
Cell survival curves look like this -
Radiation kills cells in different ways – From Steel: Basic Clinical Radiobiology
1. damaging DNA so badly that cells die in mitosis – mitotic cell death 2. damaging DNA more subtly, but enough for cells’ own genetic surveillance mechanisms to pick up the fact that the DNA is damaged and cause the cells to enter programmed cell death – apoptosis HX142 – neuroblastoma; HX156 – cervix carcinoma;
Cell survival curves • These survival curves are linearish on a log plot –
N is approximat ely proportion al to − D ln S = ln N0 • where N is the number of colonies (>50 cells) formed by irradiated cells, N0 the number formed by unirradiated cells, S the ratio of the two (‘survival’), and D is radiation dose. • But they have a rounded-off shoulder, and are clearly not exactly linear…
Cell survival curves – linear-quadratic model • Over most of the dose range, except may be below around 1 Gy, these curves are described by the ‘linear-quadratic’ model proposed by Fowler and Stern in 1958.
N = − αD - βD 2 ln S = ln N0 • This equation has been the most influential component of radiotherapy schedule design over the last 25 years.
Linear-quadratic modelling
Linear-quadratic modelling
• If you irradiate cells with a dose D1 which on its own would lead to a cell survival S(D1), then leave them for a while (say 24 hours or more) and irradiate them again with a dose D2 which on its own would lead to a survival S(D2), the resulting overall cell survival works out around –
Overall survival S = S(D1) × S(D2)
• So survival is multiplicative. Starting with N0 cells, after a first dose there are N0×S(D1) survivors left, and after a second there are N0×S(D1)×S(D2). The two doses act independently of each other – the cell-killing effect of the second is not changed by the first fraction. 1.E+00
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• To appreciate the significance of the LQ model, it’s important to describe another experimental finding (or piece of dogma, as some slightly contradictory results have been obtained over the years).
HX58 – pancreas carcinoma RT112 – bladder carcinoma
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• So survival after the two fractions is given by -
ln S = ln ( S (D1) × S (D 2 ) ) = ln (S (D1)) + ln (S (D 2 )) • working out as
(
ln S = ln (N N 0 ) = − α (D1 + D 2 ) − β D12 + D 22
)
• So, irradiating cells with a constant dose-per-fraction d given over a brief interval once every 24 hours, and delivering a total dose D in F fractions, survival will be –
ln S = − α F d − β F d 2 = − α D − β D d = - α BED
Log cell survival curves can have different curvatures
LQ modelling of a fractionated schedule • The ‘biologically effective dose’ (BED) of a schedule is defined as
BED = D ( 1 + (β α ) d )
• so that log cell survival is given by
lnS = − α BED ⇒ S = exp (- α BED) • and, more subtly, BED has the physical meaning of being the total dose delivered in a sequence of very small fractions that has the same biological (cell killing) effect as a radiation schedule which delivers total dose D in a fraction size d.
Effect of cell survival curviness on the overall survival after a sequence of fractions • For a very high α/β ratio the quadratic β component is negligible compared to α; log survival is pretty linear; and so the effect of two 8 Gy doses is roughly the same as that of one 16 Gy dose.
• The curviness of the survival curves is described by the α/β ratio, which has units of Gy. • A high α/β ratio makes for little curvature, while a low ratio describes much more curvature • The curvature has a big effect on the survival level after a sequence of many small doses.
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LQ modelling of a fractionated schedule
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• But for cells with lower α/β ratios, the survival curve bends more-and-more steeply down as the dose delivered in a single fraction increases – and so one 16 Gy fraction will do much more damage than two 8 Gy fractions.
LQ – an illustration
Hyperfractionation and α/β ratios
Consider two imaginary cells lines – (a) α/β = 10 Gy, α = 0.289 Gy-1; (b) α/β = 3 Gy, α = 0.190 Gy-1. Then survival after a single fraction of 2 Gy works out as – (a) exp(-0.289x2x(1 + 2/10)) = 0.50 (b) exp (-0.190x2x(1+2/3))
• But late complications are often characterised by α/β ratios (operational) around 3 Gy.
= 0.53 (higher than (a))
whereas after a single 4 Gy fraction survival is – (a) exp(-0.289x4x(1 + 4/10)) = 0.20 (b) exp(-0.190x4x(1 + 4/3))
• Tumors and early reacting tissue cells often have α/β ratios of around 10 Gy.
• So by hyperfractionating – delivering lower doses-perfraction but more fractions – greater tumor control can be achieved for the same late complication risk.
= 0.17 (lower than (a))
• At larger doses-per-fraction, cell lines with lower α/β ratios are killed relatively more. • Conversely, for smaller doses-per-fraction, cell-lines with higher α/β ratios are killed relatively more.
• For tumors with high α/β ratios, hyperfractionation is a very useful radiobiological modifier of clinical treatments (~ 9% improvement in HNSCC local control at 5 years – Bourhis 2006).
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A practical issue – UK hyperfractionation • While the UK CHART schedule is a pioneering example of hyperfractionation and acceleration, hyperfractionated approaches are not really widely used in Britain.
Origins of the α and β terms, and an introduction to Thames’ and Dale’s incomplete repair model of dose-rate effects Directly lethal event – prob. per cell = α × δD
• For some tumors (melanoma, probably prostate and breast) α/β is low and hyperfractionation will not be useful (though hypofractionation might). • But for many (eg NSCLC, HNSCC) hyperfractionation would be beneficial. • CHART delivers 3 fx per day over 12 days, treats through weekends, and is logistically difficult.
Sublethal event – prob. per cell = ε × δD
Second event converts sublethal damage to an indirectly lethal event – prob. per existing sublethal damage site = η × δD
• But moderate hyperfractionation, achievable by delivering 2 fx per day for at least part of longer schedules, with weekend breaks, would be easier to deliver and offer useful treatment advantages.
Modelling the dose-rate effect – an outline of Dale’s approach
Modelling the dose-rate effect – an outline of Thames’ and Dale’s approach • It’s known that sublethal damage gets repaired, being pretty much gone somewhere between 6-24 hours after irradiation. • It’s also clear that sublethal damage isn’t repaired immediately – otherwise no sublethal damage would ever get converted into lethal damage, and cell survival curves would all be straight. • The most standard modelling approach assumes that if M sublethal lesions exist at time t, µ×M×δt are repaired during the next δt , so that sublethal damage fades away exponentially ∝ exp(-µt).
Modelling the dose-rate effect – an outline of Thames’ and Dale’s approach • Binomial statistics: if there are S sites per cell that can potentially be transformed into (directly or indirectly) lethal lesions, and a probability r that each one has been transformed, then: The average total number of lethal lesions per cell NL = S × r r 1
p
S
the average number of indirectly lethal lesions per cell = ½εηR2T2 = ½εη D2 • So the average total (direct and indirect) number of lethal lesions per cell is αD + ½εη D2 or αD + βD2
Modelling the dose-rate effect – an outline of Thames’ and Dale’s approach • Now consider a fraction of longer duration T, during which sublethal damage can be repaired. • Obviously the average # directly lethal lesions per cell at T, the end of the fraction, will still be αD.
( )
=
(
− +
( )
(−
μ T
• where
2 e x p μ T
• It can also be shown that at T the average # of indirectly lethal lesions per cell will be β D2 g (µT ) 1
)
= α RT = αD
μ T 2
(
p = (1 − r )S ≈ exp(− S r ) = exp(− NL ) = exp − αD − β D 2
the average number of directly lethal lesions per cell
μ T
• Poisson statistics: if S is very large, and r is small so that the average number of lethal lesions per cell NL is a finite number, then
• It’s easy to show that by the end of the fraction -
g
The probability p of a cell surviving is just the chance of there being no lethal lesions in it, so (= −)
• Consider a dose of radiation D delivered in a brief time T, so that the dose-rate is R = D/T, where µT « 1 so that little sublethal damage is repaired during the fraction.
))
• Since g(µT) = 1 at µT = 0 and decreases with rising µT, the quadratic β component of log cell survival plots lessens as the dose-rate drops and T rises.
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The dose-rate effect
Repair, fractionation and dose-rate effects summarised
• Consequently when the dose-rate is lower, larger doses (delivered in a single fraction) are needed to reduce cell survival by a constant factor, and dose-response curves are straighter.
From Steel: Basic Clinical Radiobiology
Dose-rate 150 cGy min-1
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The curvature of cell-survival plots means that use of lower doses-perfraction (hyperfractionation) tilts the balance of damage away from endpoints with lower α/β ratios, towards those with higher ratios.
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This is useful, as many tumors have higher α/β ratios than those which characterise late complications.
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Hyperfractionation is quite a powerful tool for improving the therapeutic ratio, but is not used that widely in the UK.
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α is associated with directly lethal radiation lesions; β is associated with indirectly lethal lesions derived from sublethal damage.
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Inter-fraction intervals of at least 6 hours are required for complete repair of sublethal damage between fractions.
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The fairly slow rate of sublethal damage repair (T½ ~ 1 hour) leads to a dose-rate effect: when delivered at low dose-rates (~1cGy min-1) substantially higher doses are required to have the same effect as doses delivered at high dose-rates (~1Gy min-1) .
Dose-rate 1.6 cGy min-1
External beam or HDR brachy
LDR brachy
Repopulation – accelerated proliferation
2. Repopulation – accelerated proliferation
• Analyses of HNSCC and NSCLC data have found repopulation compensating for around 0.7 Gy per day of radiation cell killing.
• When some tumors are irradiated, their clonogen proliferation rate begins to increase.
• The effect is not clear cut though. For instance, the data shown on the last slide was just a collection of prescribed doses plotted against treatment duration.
• Mechanisms behind this effect are contentious.
• When tumor control is plotted against dose and duration, time trends are far less obvious.
• The effect itself is less contentious, though still not comprehensively characterised by clinical data.
• Nevertheless, several analyses (Withers, Rezvani, Hendry, Roberts) of HNSCC data have found accelerated repopulation running around 0.7 Gy day-1, with some indications that it doesn’t begin until around 45 weeks into treatment.
Adapted from Withers et al 1988
Repopulation – accelerated proliferation
Repopulation – accelerated proliferation
Here are some tumor control plots for the Withers HNSCC data, broken down into group 2 (T1, T1-2, T2), group 3 (T2-3, T3), group 4 (T3-4,T4) and group 0 (other T-stage combinations). Time trends are not obvious, though detailed modelling finds repopulation running at around 0.7 Gy day-1
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Repopulation – accelerated proliferation • In a 2006 HNSCC meta-analysis, Bourhis et al found hyperfractionation to be beneficial, with an ~ 9% gain in local control cf conventional fractionation • Moderate (1-2 week) schedule acceleration is also useful, with ~ 8% gain • But nothing is gained from further acceleration, which still produces ~ 8% gain cf conventional fractionation • Mucosal proliferation also accelerates during radiotherapy, allowing higher doses to be given using longer treatments without excessive early reactions • So significant acceleration is usually accompanied by reduced prescribed doses, to avoid exceeding the tolerance of the oral mucosa
Repopulation – accelerated proliferation summarised Accelerated proliferation runs at similar rates in HNSCC and oral mucosa. So while improvements can be made by accelerating unnecessarily long schedules, further acceleration achieves little, since tumor repopulation reduction is offset by dose decreases required to avoid intolerable mucositis. The standard formalism accounting for repopulation is BED = αD(1+(β/α)d) – λ(T-TK) where TK is the onset point for accelerated repopulation, and λ is the dose-per-day offset by repopulation. Mucositis discriminant analysis, showing tolerable and intolerable HNSCC schedules in a plot of dose versus treatment duration.
The oxygen effect
3. Reoxygenation and the oxygen effect •
Oxygen very substantially enhances radiation cell-killing.
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The degree of enhancement is broadly independent of the level of cellkilling in the absence of oxygen.
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Dose is effectively increased by the oxygen enhancement ratio (OER), which approaches a factor of 3 in fully oxygenated conditions.
• Enhancement is due to fixation of free-radicals created by radiation – RH in absence of oxygen RH
R˙ + H˙ ROOH in presence of oxygen For enhancement to occur, oxygen must be present at the time of irradiation, or within a few milliseconds
Figures from Steel: Basic Clinical Radiobiology
Figure from Steel: Basic Clinical Radiobiology
Oxygen effect • Poor outcomes can be expected for hypoxic tumors, since the absence of oxygen might effectively reduce dose by up to a factor of 2 or 3. • Hypoxia effects can be modified using hyperbaric oxygen or hypoxic radiosensitisers such as misonidazole. • These approaches typically improve local control rates by around 5% for HNSCC, bladder, cervix and lung patients. • Substantially more patients are thought to have hypoxic tumors.
Oxygen effect - Reoxygenation • Immediately after irradiation a tumor’s hypoxic fraction rises sharply – because well-oxygenated cells are preferentially killed. • But in animal systems the hypoxic fraction falls again quite rapidly to a level around that pre-irradiation – ‘reoxygenation’. • Mechanisms are contentious.
Figs from Jack Fowler symposium 2003
• Why then don’t hypoxia modifiers have a greater impact on treatment outcome?
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Oxygen effect – Reoxygenation
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Oxygen effect - summary
So it’s plausible that trials of hypoxia-modifiers show only 5% improvements in local control because in many patients early hypoxia is moderated by re-oxygenation. A corollary is that very short schedules may run into problems with tumor control (as well as early reactions). 300
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• So poor outcomes are expected for hypoxic tumors. • Reducing tumor hypoxia by using modifiers such as hyperbaric oxygen or misonidazole leads to ~ 5% improvement in local control at several tumor sites.
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• Oxygen powerfully enhances radiation cell killing.
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• Methods for identifying patients likely to benefit from hypoxia-modifying treatments (those with high initial tumor hypoxia and limited reoxygenation) would allow hypoxia modification to be deployed more efficiently.
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Data from a rat experiment (Moulder et al 1976), showing isoeffective doses (corrected to 2 Gy fractionation using α/β = 10 and 100 Gy) for 50% tumor control rates. For either α/β value, very short isoeffective schedules require elevated dose-levels.
4. Reassortment – cell cycle effects • Like chemotherapy, sensitivity of cells to radiation varies with position in the cell cycle. • Unlike chemotherapy, cells are at their most resistant to radiation in S-phase, probably because of enhanced DNA repair through homologous recombination, survival potentially being an order of magnitude higher than for cells in G1 and G2. • So after radiation, an increased percentage of cells will lie in S-phase. • Together with cell-cycle blocks at checkpoints following irradiation, this phenomenon has the potential to induce a degree of cell cycle synchrony amongst tumor clonogens.
5. Radiosensitivity • Studies have found correlations between tumor cell radiosensitivity (eg surviving fraction after 2 Gy) and tumor control rates, exploring variations between both different tumor types and individuals. • Likewise, correlations have been found between normal tissue damage and fibroblast and lymphocyte radiosensitivities. • Given the correlations, it’s intuitively appealing to explore the potential of dose individualisation based on radiosensitivity assays. • This approach is not yet very advanced, partly because cell survival can be difficult to measure rapidly for patients, and partly because ...
• Very short schedules may not allow enough time for reoxygenation.
Reassortment – cell cycle effects • Synchrony might be exploitable by delivering a second cytoxic agent dose at an optimal time after the first. • For instance, one cell cycle-time after irradiation many surviving cells will be back in S-phase, and if they are treated at that point using an agent with high S-phase sensitivity, enhanced cell kill might be achieved. • But disappointing results have been achieved using this approach, perhaps because cell cycle times within tumors are quite variable, causing synchrony to be lost.
Radiosensitivity and the therapeutic window • Dose response curves are sigmoidal • Tumor response curves lie to the left of normal issue curves, and tend to be less steep. • Unless dose-individualisation is smart, overall control and complication rates can be very similar to conventional doseprescription, just distributed differently amongst patients. 100
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Radiosensitivity and the therapeutic window • In particular, to individualise dose effectively, more than just a correlation (however good the p value) between outcome and radiosensitivity is required. • Tests are needed which could identify patients who are particularly likely to fail. • By targetting these specific patients with higher doses, their chances can be improved without raising the complication rate for the treatment as a whole nearly as much as if doses were raised for a larger, less focussed group. • Work is ongoing.
6. Remote (?) cell kill – the bystander effect • Evidence is piling up that radiation damage is not a completely local phenomenon – that is, some cells that are damaged or killed after irradiation may have been traversed by absolutely no photons or electrons. • Data comes from elaborate low-dose and microbeam studies which deliver such low or highly-targetted doses that only relatively few cells are directly irradiated; and from simpler experiments irradiating cells in one part of a Petri dish and exploring the effect on cells elsewhere in the dish. • Implication is that radiation action on one cell generates chemical messengers which damage other cells. • This is a change in paradigm ...
6. Remote (?) cell kill – teatment impact
Classical radiobiology – summary
• Physically, the impact on treatment depends on the distance the messenger will diffuse through tissue.
• The curvature of cell-survival plots means that hyperfractionation tilts damage away from endpoints with lower α/β ratios (often late complications), to those with higher ratios (often tumor control).
• Belyakov et al (2005) has obtained a distance of ∼ 1mm in a reconstructed skin system. • Biochemically, the agent(s) involved presumably present further targets for radiation modifiers...
• An HNSCC meta-analysis found hyperfractionation gives ~ 9% gain in local control cf conventional fractionation • Moderate (1-2 week) schedule acceleration usefully limits accelerated tumor repopulation, HNSCC meta-analysis showing an ~ 8% gain • Little is gained from further acceleration, which requires dose-reduction and still produces ~ 8% gain compared to conventional fractionation
Classical radiobiology – summary • Tumor hypoxia-modifiers produce ~ 5% improvement in local control for several cancers. • Identifying patients likely to benefit from hypoxiamodifiers would allow more efficient deployment.
Classical radiobiology
• Very short schedules may limit reoxygenation. • Disappointing results have been achieved using cell synchrony approaches. • Dose-individualisation generally requires assays with good sensitivity and specificity.
Thank you for your attention
predictive
• Bystander effects occurring on a 1 mm length-scale invivo will have limited physical impact on treatments.
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