Marco Trovò CRO-Aviano

From pre-clinical research to clinical practice in radiation therapy: new predictive factors and biological markers to predict normal tissue toxicity after radiation therapy of the lung. Marco Trovò CRO-Aviano Reggio Emilia 16 Aprile 2010

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

Let s start from the beginning: radiobiology The tolerance of normal tissues to radiation limits the dose that can be safely delivered in the treatment of malignancies. For many years, the only option available was to limit the dose and volume of irradiated tissue. Theoretical curves showing the probability of tumor control and normal tissue complications. Both curves have a threshold and are log sigmoid in nature. The art of radiotherapy is to increase the distance between these two curves; that is, to derive a therapeutic benefit. If the normal tissue damage curve is to the left of that for TCP, tumor control is unlikely without unacceptable normal tissue complications.

Importance of identification of predictive factors able to select patients at increased or decreased risk of treatment-related injury.

Importance of identification of predictive factors able to select patients at increased or decreased risk of treatment-related injury. SBRT 0-3 months 3-6 months 6-18 months 24 months Baseline 3 months after SBRT 5 months after SBRT 9 months after SBRT 25 months after SBRT M, 75 y/o, NSCLC Patchy GGO Diffuse consolidation Dense consolidation and bronchiectasis Dense consolidation and bronchiectasis

Importance of identification of predictive factors able to select patients at increased or decreased risk of treatment-related injury. SBRT 0-3 months 3-6 months 6-18 months 24 months Baseline 1 month after SBRT 5 months after SBRT 12 months after SBRT 24 months after SBRT F, 51 y/o, NSCLC No radiologic injury No radiologic injury Scar-like fibrosis Scar-like fibrosis

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

Traditional predictive factors of lung toxicity Age Patient specific Gender Smoking Comorbidities PS Treatment specific Dose Dose per fraction Fractionations Dosimetric Parameters

Traditional predictive factors of lung toxicity Dosimetric parameters V20 V30 MLD lung volume that receive a dose > 20 Gy NTCP models

Traditional predictive factors of lung toxicity V20 Pneumonitis GRADE 2 V20 <22% 22-31% 32-40% >40% Rischio 0 7% 13% 36% Graham IJROBP 1999

Traditional predictive factors of lung toxicity

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

Superior/Inferior Position Non-Traditional predictive factors of lung toxicity Tumor location associated with risk of 1 0.8 0.6 0.4 0.2 Right Lung: 47/137 (34.3%) pneumonitis Left Lung: 22/91 (24.2%) Superior 25%: 15.9% (7/44) Middle 25%: 30.2% (42/139) Inferior 50%: 44.4% (20/45) 0 0 0.2 0.4 0.6 0.8 1 Right/Left Position Bradley, IJROBP 2007

Non-Traditional predictive factors of lung toxicity Univariate Correlations Predicting Radiation Pneumonitis Parameter WU (Spearman s) MLD 0.18 V 20 V 13 0.18 D 35 0.18 0.19 D 15 0.11 GTV-SI GTV-AP 0.23 0.02 Bradley, IJROBP 2007

Non-Traditional predictive factors of lung toxicity WU/RTOG 9311: Pneumonitis Data Multi-metric Modeling [MLD, COM-SI] [V65, COM-SI] [D25, MLD] Models [MLD, PreTxChemo] [D35, V70] [D35, COM-AP] [ V25, V75] [MLD, COM-LAT] 0 20 40 60 Percentage frequency

Non-Traditional predictive factors of lung toxicity Nomogram on pneumonitis probability Bradley, IJROBP 2007

Non-Traditional predictive factors of lung toxicity High-dose Heart Irradiation is a Statistically Significant Risk Factor for Radiation Pneumonitis J. O. Deasy 1, M. Trovo 2, E. X. Huang 1, Y. Mu 1, I. El Naqa 1, and J. D. Bradley 1 1 Dept. of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 2 University of Milan, Milan, Italy

Non-Traditional predictive factors of lung toxicity Heart volumes of WUSTL archived plans were recontoured within CERR by a single physician (n = 209, with 48 RP events). Heart and normal lung (lung minus gross tumor volume) dose-volume parameters were extracted for further modeling using CERR. Evaluated factors included: clinical (age, gender, race, performance status, weight loss, smoking, histology) dosimetric parameters for heart and normal lungs (D5-D100, V10-V80, mean dose, maximum dose, and minimum dose) treatment factors (chemotherapy, treatment time, fraction size) location parameters (heart center-of-dose, sup-inf within the heart; and center-of-target mass within the normal lungs.) Deasy, ASTRO 2008

Non-Traditional predictive factors of lung toxicity Highest univariate correlations Variable Spearman Corr. Significance D5_Heart 0.256 <0.0002 D10_Heart 0.24 <0.0003 V70_heart 0.239 <0.0003 geud_heart (a=10) Maximum Heart Dose Superior-Inferior position of GTV 0.249 0.227 0.219 <0.0001 <0.0006 <0.0008 Deasy, ASTRO 2008

Non-Traditional predictive factors of lung toxicity Comparison between multivariate model prediction and risk group Comparison of predicted risk of RP by four parameter model (in order of selection: D10_heart, D30_heart, Mean lung dose, DCOMSI_heart) and actual incidence in the population. The patients are binned according to predicted risk of RP by the model (equal patients in each bin). Deasy, ASTRO 2008

Non-Traditional predictive factors of lung toxicity D10=58Gy D10: hottest spot at 10% of organ volume

Non-Traditional predictive factors of lung toxicity Scatter Plot between D10_Heart and GTV_COMSI Note increasing risk with high-dose heart irradiation that dominates previously reported lung location effect (Hope, IJROBP 2006). Deasy, ASTRO 2008

Dosimetric parameters (V20, MLD, NTCP) and tumor-related factors allowed for patient-stratified dose escalation both for locally-advanced and for early-stage NSLCL leading to a major improvement of the outcome!

In Locally-Advanced NSCLC, by a dosimetricparameter patient stratification (V20)

Graham Retrospective studies Claude Prospective validations DOSE-ESCALATION RTOG 93-11 RTOG 01-17 RT 77-83 Gy CRT 74 Gy RANDOMIZED TRIAL!!! RTOG 06-17 60 Gy VS 74 Gy

In early-stage NSCLC, by the introduction of dose-perfraction escalation (Stereotactic-Body Radiation Therapy) ASTRO meeting 2008 100 Local failure 1.0 90 0.8 80 70 Control rate 0.6 0.4 0.2 Overall local control rate Survival rate rate(%) 60 50 40 30 Local control by maximum dose p=0.07 20 0.0 0 10 20 Time (Months) 30 40 50 10 Maxdose>=67 Gy (n=27) MaxDose < 67 Gy (n=62) 0 0 5 10 15 20 25 30 35 Time (Years)

Author N of patients Pneumonitis Uematsu 66 Grade 3 0% Nagakawa 22 Grade 3 0% Onishi 257 Grade 2 5.4% Wulf 61 Grade 3 3% Hara 23 Grade 3 4% Lagerwaard 206 Grade 3 3% Timmerman 37 Grade 3 5.4% Bradley 67 Grade 2 3%

Lung Cancer 2009 Radiot.Oncol. 2007 IJROBP 2006

Early changes: pneumonitis Before 6 months from SBRT 1. Diffuse consolidation 2. Diffuse ground-glass opacity (GGO) 3. Patchy consolidation and GGO 4. Patchy GGO 5. No changes

SBRT Early changes M, 85 y/o, NSCLC; baseline 1.DIFFUSE CONSOLIDATION Diffuse and homogeneous increase in parenchymal attenuation, obscuring vessels and bronchi 4 months after SBRT Air bronchogram

SBRT Early changes M, 80 y/o, NSCLC; 5 months after SBRT 2. DIFFUSE GGO Hazy increase of parenchymal attenuation... L M, 93 y/o, H&N met; 5 months after SBRT

SBRT Early changes F, 78 y/o, NSCLC; 6 months after SBRT 3. PATCHY CONSOLIDATION AND GGO Areas of hazy and dense increased parenchymal attenuation interspersed in normal lung.. Baseline L F, 66 y/o, NSCLC 5 months after SBRT

SBRT Early changes F, 75 y/o, NSCLC; 4 months after SBRT 4. PATCHY GGO L Areas of hazy increased parenchymal attenuation interspersed in normal lung L = lesion.. M, 60 y/o, NSCLC; 3 months after SBRT

SBRT Early changes M, 78 y/o, NSCLC; baseline 5. No change 4 months after SBRT..

Late changes: fibrosis (after 6 months after SBRT) 1. Modified conventional pattern 2. Mass-like pattern 3. Scar-like pattern 4. No change

SBRT Late changes M, 75 y/o, NSCLC; 7 months after SBRT 1.MODIFIED CONVENTIONAL PATTERN Parenchymal consolidation, volume loss, bronchiectasis M, 75 y/o, NSCLC; 9 months after SBRT..

SBRT Late changes 2. MASS-LIKE PATTERN DIFFICULT DDx WITH RECURRENCE!!! Focal area of increased density F, 52 y/o, NSCLC; 11 months after SBRT

SBRT Late changes M, 80 y/o, NSCLC; 10 months after SBRT 3. SCAR-LIKE PATTERN Linear opacity and volume loss

SBRT Late changes F, 63 y/o, NSCLC; baseline 4. No change 11 months after SBRT 22 months after SBRT

Lung Cancer 2009 1. No correlation between lung injuries and tumor dimension or radiation dose 2. No correlation between lung injuries and age, smoking habits, comorbidities, PS, or emphysema 3. Steroids administration did not impact on the development of late fibrosis, but seems to delay early pneumonitis

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

2. There are no dosimetric models to predict radiation lung injury after SBRT! Baseline 3 months after SBRT 5 months after SBRT 9 months after SBRT 25 months after SBRT

1. The accuracy of prediction of lung tissue toxicity derived from solely dosimetric models is insufficient! Besides dosimetric factors, biological parameters might be considered. Many studies looked at correlations between risk of radiation lung injury and variation in profibrogenic and pro-inflammatory cytokines.

Initial tissue damage from RT is generated by the direct action of reactive oxygen species (ROS) on DNA Simplified model of the complex network of interacting processes and signals in the pathogenesis of radiation lung injury

Within this cascade, there are dominant cytokines and growth factors, which are up-regulated and peak at different time points. Studies were conducted to investigate the feasibility of these biological markers as predictors of radiation-induced lung injury.

Clinical studies measuring TGF-B in patients undergoing RT, to predict radiation pneumonitis or fibrosis

Clinical studies measuring other than TGF-B in patients undergoing RT

Biological markers predicting patients at risk of lung injury are awaited: Allow for patient-stratified dose escalation maximizing individual therapeutic gain Early diagnosis of early radiation pneumonitis Diagnostic tool to distinguish radiation pneumonitis from differential diagnosis

Outline Introduction Traditional predictive factors of lung toxicity Non-traditional predictive factors of lung toxicity Biological markers as predictors of radiation toxicity Trial proposal

Trial proposal at CRO-Aviano Objectives: To describe early and late lung radiographic injuries following helical-imrt To correlate such injuries to patient and treatment characteristics To implement a NTCP model to estimate the risk of early and late lung injuries To identify biological markers predicting the risk of radiation injuries To identify genetic markers (gene polymorphism) predicting normal tissue toxicity and tumor control

Trial proposal at CRO-Aviano To identify biological markers predicting the risk of radiation injuries: Multiplex assays to facilitate the simultaneous evaluation of multiple proteins: to generate a sophisticated normal tissue injury risk profile for an individual patient! Flexible analyzer based on the principles of flow cytometry enables you to multiplex (simultaneously measure) up to 100 analytes in a single microplate well, using very small sample volumes.

Thank you for your attention marco.trovo@cro.it