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PERSONALISED MEDICINE IN
RADIATION ONCOLOGY
Dr. Ashutosh Mukherji,Associate Professor,
Department of Radiation Oncology,Regional Cancer Centre,
JIPMER
Personalized MedicineThe ability to offer The Right Drug To The Right Patient For The Right Disease At The Right Time With The Right Dosage
Genetic and metabolic data will allow drugs to be tailored to patient subgroups
"Here's my sequence...”
Personalized or Predictive Medicine
Patients with same diagnosis Respond to treatment
No response to treatment
Experience adverse events
GOALS OF RT TREATMENT PLANNING
maximum dose to tumor bearing volume
uniform dose to tumor bearing volume
minimum possible dose to normal structures
Primary obstacles in conventional RT planning and delivery Uncertain true spatial extent of the disease Inadequate knowledge of the exact shapes
and locations of normal structures Lack of tools for efficient planning and
delivery Hence………. large safety margins to adequately
cover the target volume
The new face of radiotherapy Since early 1990s, radiotherapy has
become increasingly technology oriented
This has resulted in improving the local control rates and minimizing morbidity
Personalisation of Radiotherapy
Individualisation of irradiation techniques and
fields
Patient selection and assessment of
response / tolerance
Better Imagin
g New Genomi
cs
New drug-RT interacti
ons
Biomarkers
Newer treatment techniques
Teletherapy 3D Conformal Radiotherapy Intensity Modulated Radiotherapy (IMRT) Stereotactic irradiation Image Guided Radiotherapy (IGRT)
Brachytherapy Advanced High Dose Rate systems Sites previously considered not-possible are
easily now
What is IMRT?
Intensity modulated radiotherapy Standard flat
fields are modulated
This modulation can be created with inverse planning systems
Delivery of IMRT fields : Dynamic MLC
Leaf A Leaf B
Position
Intensity
Continuous modulation
IGRT: to overcome organ motion and setup errors
TumorCross-sectional View
of Patient’s Chest
Tumor
Some motion is mostly Anterior / Posterior
Some motion is mostly Superior / Inferior
All tumor motion is Complex
Tumor Motion During Respiration
All tumor motion is complex
Image Acquisition with breathing phase
Cone Beam CT Mode – Axial (z) Geometry
z
Transaxial ~ Transaxial
For single- and multi-slice CT scanners the slices are approximately parallel. This does not apply to Cone Beam CT.
Cone Beam CT Mode – Axial (z) Geometry
z
Transaxial ~ Transaxial Cone Beam
Volumetric Image
17 cm
CT Scan SPECT IMRT Treatment
Functional Imaging - Nuclear Medicine
PTV
PTV
GTV
Hypoxia• PET (F-miso)
Tumor Growth• PET (IUDR)
Tumor Burden• MRI• MRS (choline/citrate)
Functional Target Volume?Biological Target Volume?
GTV
What is the Target?- Functional Target Volumes
Cytochrome P450 genotyping test Enzyme group ‘cytochrome P450’ (CYP450 Many types of medications(including antidepressents,
anticoagulants, proton pump inhibitors, etc) Determine dosing and effects of these drugs.
Thiopurine methyltransferase test Thiopurine Thiopurine methyltransferase (TPMT)
UGT1A1 TA repeat genotype test Irinotecan (Camptosar) UGT1A1 enzyme
Dihydropyrimidine dehydrogenase test 5-flourouracil (5-FU) Dihydropyrimidine dehydrogenase enzyme Responsible for breaking down 5-FU
Here are some examples!
Biomarker Application
Her-2/neu receptor Select Herceptin (trastuzumab) for breast cancer
BRCA1/2 Breast and ovarian cancer inherited risk, prophylactic tamoxifen and surgery
Transcriptional profile – 21 genes Avoid use of chemotherapy in breast CA patients with low risk of recurrence
CYP2D6/CYP2D19 Guide prescribing/ adjust dose of ~25% of commonly used drugs
VKOR/CYP2C9 Dosing of warfarin
From Bench to Bedside:Complexity of the Human Being
Biomarkers related to the host
Clinical Outcomes-Hard outcomes (OS/DFS)-Soft outcomes (toxicity/QOL)
Biomarkers of tumor
Environmental Modifying Factors
Treatment Factors
PsychosocialCultural, Economic
Non-causal Prognostic Factors
Causal Prognostic Factors
Adapted from Liu et al, 2006Radio-genomics
Pathways and Mechanisms of Tissue response to Irradiation
Radiogenomics & Personalised RT
60% cancer patients require radiotherapy The 3 main predictors of response to RT are:
Intrinsic radiosensitivity Tpot (tumor proliferative potential) Tumor oxygenation
These can be studied in vitro by: Assessing SF2 (surviving fraction at 2 Gy exposure) Clonogenic survival assays Determining Tpot Measuring tissue oxygenation using electrodes
Measuring SF2 by clonogenic survival
assays Has been the gold standard
Some data exists to show relation between SF2 and inherent radio sensitivity of tumor tissue
However its clinical application has not been widespread because of the difficulties of in vivo testing as well as because of further interactions with environmental factors and signalling / transduction pathways.
Cancer Control, April 2008: Vol 15; No. 2
Clinical response and oxygenation
Well recognised clinical theory since action of irradiation depends on generation of free radicals.
Eppendorf probe most successful one used.
Extensive studies on hypoxia in cervical cancer causing poor response.
This method limited by accessibility of tumor (in head and neck / cervix cancers).
Hypoxia inducible protein- alpha now being studied; considered better biomarker.
Biology of Tumor HypoxiaHypoxicRegion
Blood Vessel
O2 / DrugConcentration
Gene/Protein Regulation
Increased Glycolysis
Increased Angiogenesis
Increased Genomic Instability
Selection of Apoptosis Resistance
Chemo/Radio-therapy Resistance
From Meijer et al Clin Cancer Res, 18: 5585-5594, 2012
HIF-1 (Hypoxia-inducible factor-1) enables tumour cells to survive hypoxia
Role of TpotBasically study of potential doubling time of tumourLarge studies by EORTC shown little or no correlation with survival.Is a weak predictor of outcome
Correlation of DNA End-Binding Complexes With Cellular
RadiosensitivityDNA damage activates many signal transduction cascades like ataxia telangiectasia mutant (ATM) and DNA-dependent protein kinase pathways (DNA-PK)assay to analyze DNA end-binding complexes: identified rapidly migrating ATM-containing band (B and A), the density correlated with radiosensitivity.
Predicting radio-sensitivity from genetics
It is estimated that nearly 80% of inter-individual variation in normal tissue response to radiation might be due to genetic factors (Turesson et al. 1996). Radiation therapy also has a relatively narrow therapeutic index (Turesson 1990; Bentzen et al. 2008).
Therefore, understanding the biology might help us to maximize radiation efficacy in the tumor, while minimizing side effects in normal tissues.
Several radio-genetic studies have shown that genetic polymorphisms in genes within known radiation response pathways are significantly associated with radiosensitivity.
These include endogenous oxidative stress defense, inflammatory response, cytokine activity related to fibrosis, DNA damage signaling, cell cycle control, and DNA repair
Predicting radio-sensitivity from genetics
Apoptosis has been associated with the ATM-p53-Bax-Cytochrome c-Caspases pathway
Mitotic catastrophe involves the p53-Caspases-Cytochrome-C cascade
For necrosis, TNF (alpha) -PARP-JNK-Caspases pathway is involved
MYC-INK4A-ARF-p53-p21 pathway has been implicated in senescence.
In autophagy, the PI3K-Akt-mTOR cascade is important
genome-wide association study (GWAS) to identify biomarkers to predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs).
Basal gene expression levels and 1.3 million genome-wide single nucleotide polymorphism (SNP) markers were assayed for all 277 human LCLs.
Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression, and AUC data, performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays.
A total of 270 expression probe sets were associated with radiation AUC with P < 10–3. The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes, which were also associated with radiation AUC (P < 10–3).
Expression of five genes: C13orf34, MAD2L1, PLK4, TPD52, DEPDC1B, involved in radiation-induced response.
Predicting radio-sensitivity from genetics
A study from Singapore proposed a Radio-sensitivity Index based on identification of genes as a biomarkers.
In sites such as breast, colon, melanoma, non-small cell lung, ovarian, renal and prostate cancer.
A ten gene network thought to play a central role in determining radio-phenotype.
Cellular radio-sensitivity as a linear function of gene expression for the ten genes was quantified by cell survival.
Is currently undergoing further clinical validation under US FDA for clinical use. This RSI can predict therapeutic benefit independent of the disease site.
Torres Roca JF, Eschrich S, Zhao H et al. Prediction of radiation sensitivity using a gene expression classifier. Cancer Res.65(16),7169–7176 (2005).
Predicting radio-tolerance and side effects from
genetics ATM gene generalized radio-sensitivity in patients with
ataxia-telangiectasia, and toxicity in patients with breast, prostate, and lung cancers treated with radiotherapy;
XRCC gene late fibrosis in patients with breast cancer post radiation therapy, and post-irradiation mucositis, dermatitis, and dysphagia in patients with head and neck cancers;
TGFbeta cytokine inhibits proteolytic activity essential to cell maintenance.
Current understanding is that radiosensitivity is an inherited polygenic trait, dependent on the
interaction of many genes/gene products involved in multiple cell processes
Bioscience November 2015
Comet assays of circulating lymphocytes also give valuable information on radiation induced tissue damage patterns
Cancer Pharmacogenetics
Cancer Pharmacogenomics
Biomarkers Predictive for Drug Outcomes
Biomarkers Predictive for Treatment Outcomes
+
Personalisation of radiotherapy delivery
GERMLINE
SOMATIC or TUMOUR
PROTEINS, IMAGING
RADIATION THERAPY
Cancer Patients
Germline / Somatic Genotype
Prediction of Drug Efficacy
Incorrect Genotype
Assignment
• Improved Outcomes
• Enhanced Response
• Minimize Toxicity Harms of
Subsequent Management
Options
Treatment Decisions
Analytic
Validity
Clinical Validity Clinical Utility
Overarching Question
Prediction of Metabolism
Prediction of Adverse Drug
Reactions
Analytic Framework + Key Questions for Evaluating Genomic Tests in a Specific Clinical Scenario
Cancer Patients
Germline / Somatic Genotype
Prediction of Drug Efficacy
Incorrect Genotype
Assignment
• Improved Outcomes
• Enhanced Response
• Minimize Toxicity Harms of
Subsequent Management
Options
Treatment Decisions
Analytic
Validity
Clinical Validity Clinical Utility
Overarching Question
Prediction of Metabolism
Prediction of Adverse Drug
Reactions
Analytic Framework + Key Questions for Evaluating Genomic Tests in a Specific Clinical Scenario
THANK YOU