Robust Biologically Guided Radiation Therapy (BGRT) Robert D. Stewart, Ph.D. Associate Professor, Radiation Oncology and Medical Physics U i University it off Washington W hi t Medical M di l Center C t Department of Radiation Oncology 1959 NE Pacific Street Seattle, WA 98195-6043 206-598-7951 office 206-598-6218 fax
[email protected]
Presented at the
4th Modelling of Tumors (MOT) 2012 Meeting (August ( 2-4)) Date and Time: Friday August 3, 11:00 to 11:30 am Location: Hotel Grand Chancellor, 65 Hindley Street, Adelaide, South Australia Website: http://www.rah.sa.gov.au/cancer/mot.php © University of Washington Department of Radiation Oncology
© University of Washington Department of Radiation Oncology
Learning Objectives Rationale for BGRT Are existing biological models “good enough” for
clinical applications? • Some of the challenges • Limitations and applicability of BED and EUD concepts with a focus on intra- and inter-patient heterogeneity
Examples • Equivalent prescriptions • Plan ranking and comparison with EUD
This Presentation and Supplemental Slides • http://faculty.washington.edu/trawets/ http://faculty washington edu/trawets/ Presenter has no conflicts of interest to disclose
Slide 2
Slide 3
© University of Washington Department of Radiation Oncology
Why isn’t EBRT more successful? Uncertainty in boundary of primary tumor Inability to delivery a tumoricidal dose Migration of diseased cells to other body parts Critical Organ
Overt Disease
Dose escalation not always possible
Subclinical Disease
© University of Washington Department of Radiation Oncology
Slide 4
Motivation for BGRT How do we get the most bang for our buck (dose (dose)? )?
Outcome Prediction or “Biological Metrics” Metrics” A way to rank the relative efficacy of alternate and competing treatments
When local control cannot be achieved through dose escalation, only RT option is to move the dose around in space and/or time.
Slide 5
© University of Washington Department of Radiation Oncology
Repair (↓) Repopulation (↓) Redistribution (↑) Reoxygenation (↑)
Lo ocal Tumo or Controll
Four R’s of Radiobiology (conventional ti l wisdom i d )
T t l Treatment Total T t t Time Ti
Slide 6
© University of Washington Department of Radiation Oncology
Physics → Chemistry → Biology → Clinic Chemical 10-3 s Repair
Absorbed Dose
O2 fixation Ionization Excitation
Radiation
Late Effects (fibrosis, …)
106 s
s
108 s
103 s
Inflamatory Responses
Chronic hypoxia (> 11--2 h)
Heritable Effects
105 s
Cell Death
Self renewal and Differentiation
2nd Cancer Clonal Expansion
(BER, NER, NHEJ, …))
10-6 s
Angiogenesis and Vasculogenesis
104 s
Enzymatic Repair
DNA damage
Loss of Function and Remodeling
108
102 s
Acute hypoxia
10-18 to 10-10 s
Early Effects (erythema, …)
Correct Repair p
1 Gy ~ 1 in 106
Chronic hypoxia (> 44--10 h?)
Incorrect or Incomplete Repair
104 s
105 s
105 s Germline
107 s
Small-- and largeSmall large-scale mutations Neoplastic Transformation
(point mutations and chromosomal aberrations))
Somati c cells
Slide 7
© University of Washington Department of Radiation Oncology
The LQ in Radiation Therapy Inaccurate and too simplistic (compared ( to known biology))
S ( D ) = exp ( −α D − β GD 2 ) one--hit one hi d damage
Dose-rate D Doset and dd dose-fractionation dosef ti ti effects (“dose (“ protraction factor”) ”)
inter--track damage interaction inter
Parameters (e.g., ( α and β)) derived from analysis of clinical outcomes are uncertain and averaged over a heterogeneous tumor and patient population JF Fowler, l R Chappell, Ch ll M Ritter, i IJROBP 50 50, 1021-1031 (2001) α = 0.039 Gy-1 α/β = 1.49 Gy S = 1.159 × 10-3 (37 × 2 Gy))
JZ Wang, M Guerrero, XA Li, IJROBP 55, 194-203 (2003) α = 0.15 Gy-1 α/β = 3.1 Gy
(4X higher)) (2X higher))
S = 2.677 × 10-8
(104 smaller))
Slide 8
© University of Washington Department of Radiation Oncology
SF for a Heterogeneous Cell Population Can’t use a single (average) ( ) set of LQ radiation sensitivity parameters (α, α/β)) to predict overall shape of dose--response curve dose
100
Survviving Fractio on
Resistant (10%) ( ) 10-1
S ≠ exp( exp(--αD-βGD2) Average
Sensitive (90%) ( )
Five Reasons ((manyy others possible) p )
10-2
S = f S1 + (1(1-f )S2 10-3 0
1
2
3
4
5
6
7
8
9
10
Genomic Instability Repair Repopulation Reassortment Reoxygenation
Absorbed Dose (Gy)
But may be reasonable to extrapolate from a known point?
© University of Washington Department of Radiation Oncology
Poisson Tumor control probability (TCP) Most widely used model assumes that the distribution of the number b off ttumor cells ll surviving i i a ttreatment t t is i adequately d t l described by a Poisson distribution
TCP = exp{-ρVS(D)} Chance no tumor cells survive a treatment that delivers total dose D
ρ = number of tumor cells per unit volume (< 109 cells cm-3) V = tumor volume (GTV? CTV? PTV?)) product ρV = pre pre--treatment number of tumor cells Typical uncertainty? Factors as large as 103 to 106! Eradication of some cells, such as cancer stem cells, may be far more important than the eradication of others (effective ( ρ
