Personalised medicine in rt dr. ashutosh

PERSONALISED MEDICINE IN
RADIATION ONCOLOGY
Dr. Ashutosh Mukherji,
Associate Professor,
Department of Radiation Oncology,
Regional Cancer Centre,
JIPMER

Personalized Medicine
The ability to offer
 The Right Drug
 ToThe Right Patient
 ForThe Right Disease
 AtThe RightTime
 WithThe Right Dosage
Genetic and metabolic data
will allow drugs to be tailored
to patient subgroups

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
Imaging
New
Genomics
New drug-
RT
interactions
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

Personalised medicine in rt dr. ashutosh

Delivery of IMRT fields :
Dynamic MLC
Leaf A Leaf B
Position
Intensity
Continuous modulation

IGRT: to overcome
organ motion and
setup errors

Tumor
Cross-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
Psychosocial
Cultural, Economic
Non-causal
Prognostic Factors
Causal Prognostic Factors
Adapted from Liu et al, 200
Radio-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 Hypoxia
Hypoxic
Region
Blood
Vessel
O2 / Drug
Concentration
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 ofTpot
Basically study of
potential doubling time of
tumour
Large studies by EORTC
shown little or no
correlation with survival.
Is a weak predictor of
outcome
Correlation of DNA End-Binding
Complexes With Cellular Radiosensitivity
DNA 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

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.

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
ToxicityHarms 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

Personalised medicine in rt dr. ashutosh

More Related Content

What's hot (20)

Similar to Personalised medicine in rt dr. ashutosh (20)

More from Ashutosh Mukherji (20)

Recently uploaded (20)

Personalised medicine in rt dr. ashutosh

Editor's Notes