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 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


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.


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

THANK YOU

personalised medicine in rt dr. ashutosh

Health & Medicine