Dr Yvonne WallisConsultant Clinical Scientist

West Midlands Regional Genetics Laboratory

Personalised Therapy/Precision Medicine Selection of a therapeutic drug based on the presence or absence

of a specific drug target (biomarker) in the tumour that renders the tumour cells sensitive or resistant to therapy

Diagnosis TreatmentPrognosis Monitoring

Cancer Clinical Pathway

Benefits of Personalised Therapy PATIENTS/CLINCIANS

Access to more effective targeted treatments Fewer adverse events

SCIENTISTS/CLINICIANS Biomarker knowledge - improved drug development More efficient clinical trial design

NHS Right patient, right drug, right time Time and cost savings

Cancer Biomarkers

Cancer is a genetic disease Acquired changes in oncogenes and tumour suppressor genes Control cell division, DNA repair, angiogenesis, cell death Driver mutations - oncogenesis

“Same site” tumours Variation in genetic make-up Different responses to treatment

Personalised cancer care Tumour classification based on genetic make-up rather than

tumour site Management based on specific genetic biomarkers

Early examples of Personalised Therapy

Breast cancer ERBB2 oncogene expression Amplification 35% breast tumours/Poor prognosis Herceptin (first solid tumour monoclonal antibody)

Non small cell lung cancer EGFR mutation status assessed Anti-EGFR tyrosine kinase inhibitors

Sensitising mutations (exons 19 and 21) Resistance mutations (exons 18 and 20)

Genetic Biomarkers – Somatic variants

Analysis of tumour DNA FFPE – formalin fixed paraffin embedded tissue FF – Fresh frozen ctDNA – circulating tumour DNA

Genetic testing technologies

Single Target Tests Multiple Target Test

Pyrosequencing Next Generation Sequencing

ddPCR

Sanger sequencing

FISH

Challenges - Heterogeneity

Tumour heterogeneity Inter- and intratumour genetic

subclones Multi-region sampling

Primary vs metastasis Circulating tumour DNA

Cell free tumour-derived DNA in plasma

Reflects whole tumour genome Levels reflect tumour burden

Tissue heterogeneity Normal and tumour cell types Background wild type DNA “Mask” tumour mutations Macrodissection to enrich for

tumour

Challenges - FFPE Formalin Fixed Paraffin Embedded tissue

Most widely practiced method for clinical sample preservation Formalin preserves the cytoskeletal and protein structure of the cells Allows the cells to be stained and abnormalities to be detected FFPE workflows embedded in clinical pathways for cancer diagnosis

Possible to extract DNA/RNA from these tissues Enabling molecular profiling Poor quality templates

Nucleic acids are highly fragmented Presence of contaminates Non-reproducible cytosine deamination artefacts occurs

in one strand only

Challenges - FFPE DNA QCQuantity• dsDNA• Fluorometry – Qubit/Picogreen

Quality• Fragmentation – Bioanalyzer, tapestation• “Amplifiability” - qPCR

Contaminants• UV absorbance – Nanodrop• qPCR

Challenges – Genetic complexity

Wide spectrum of somatic variation

SNVs, indels, CNVs, structural variants, “mutational signatures”

Multiple changes important to detect for effective personalised therapy

Multiple technologies used

Circos plot generated from whole genome sequencingGenome wide visualisation of somatic variation

Challenges - Interpretation

Standardisation of interpretation & reporting Four tiered system to categorise somatic variants Based on impact on clinical care –

Predicts sensitivity/resistance to therapy https://www.mycancergenome.org/ Supports inclusion in a clinical trial https://clinicaltrials.gov/ct2/home Influences prognosis, assists diagnosis Warrants surveillance measures for early detection

Classification Tier I: Strong clinical significance Tier II: Potential clinical significance Tier III: Unknown clinical significance Tier IV: Benign/likely benign

Next Generation Sequencing –Emerging technology in personalised medicine

Advantages Massively parallel sequencing – multiple targets simultaneously Sensitivity (<1%) – high read depth potential Broad spectrum of mutations Difficult template tolerant Low cost per base High throughput

Basic Steps of NGS: DNA to Data

Sequencing• Application

dependent• Single read• Paired-end analysis• Mate-pair analysis• Produces “reads”

Bioinformatics• Base calling• Alignment• Variant calling• SNVs• CNVs• SVs

Further Bioinformatics• Annotation• Variant Interpretation• Assess pathogenicity

Specimen• Germline• Blood• Saliva/other tissue

• Somatic• Tumour• Fresh Frozen/FFPE

Nucleic acid extraction• DNA • RNA• Template dependent• Automation options

Library preparation• “Target enrichment “• Gene Panel• Whole Exome• Whole Genome

NGS strategies Strategy: Panel Test Whole Exome Whole GenomeTarget: SELECTED genes All CODING genes WHOLE DNA sequence

Size: 10-500 genesVariable size

~20,000 genes 30 million bases

Whole genome 3000 million bases

Coverage: >99% target coverage

>98% coverage >95% coverage

Depth/Sensitivity: +++ ++ +

Throughput: +++ ++ +

Data storage: + ++ +++

Cost: £ ££ £££

Variants: Gene related variants

30,ooo variants 3 million variants

Interpretation: Actionable mutations

Novel variantsIncidental findings

Novel variantsIncidental findings

Sensitivity of NGS –Confidence for low level variant detection

Tumour %

% of mutant reads if variant at 10% level

Ratio of mutant reads

Sequencing depth required to obtain 10 mutant reads

5% 0.5% 1/200 2000

10% 1% 1/100 1000

20% 2% 1/50 500

40% 4% 1/25 250

50% 5% 1/20 200

100% 10% 1/10 100

0

500

1000

1500

2000

2500

5 10 20 40 50 100

Rea

d de

pth

Tumour %

CR-UK Stratified Medicine Programme• CR-UK the world's largest independent cancer research charity

• SMP is their hope to make personalised medicine standard practice of care for all cancer patients

• Launched in 2010

Our vision is to establish a national molecular diagnostics service delivering high quality, cost effective tests for patients, with routine consent for the collection, storage and research use of genetic, treatment and outcomes data.

Phase 1 (SMP1)

• Sep 2011 – Aug 2013• 9000 patients• Breast, colorectal,

lung, melanoma, ovarian, prostate

• 8 Clinical Hubs• 3 Technical Hubs• Proof of principle• NGS panel vs

single tests

Phase 2 (SMP2)

• Sep 2013 >• 2000 patients• Lung (NSCLC)• 18 Clinical Hubs• 3 Technical Hubs• 28 gene panel• Linked to National

Lung Matrix Trial

Stratified Medicine Programme

Cardiff

Cardiff

University Hospital of

Wales

Morriston

Singleton

Royal Gwent

Velindre

Manchester

The Christie

Wythenshawe

SalfordRoyal

Pennine Acute

Glasgow

Royal Infirmary

Western Infirmary

Southern General

Golden Jubilee

Gartnavel

Victoria Infirmary

Stobhill Hospital

Birmingham

Birmingham

University Hospital

City

Edinburgh

Western General

Royal Infirmary

Cambridge

Addenbrooke's

Papworth

RMH

RMH

Marsden

Royal Brompton

Leeds

St. James's

General Infirmary

Technology HubsClinical HubsFeeder Hospitals

Infrastructure versus population density

The SMP1 Network: 3 Technology Hubs, 8 Clinical Hubs, 26 Feeder Hospitals

SMP2 Structure

SMP2 NGS Test– Panel linked to Matrix Trial– Detects a range of variations across 28 genes– Multiple changes in the same patient– For trial eligibility the wild type status of

some genes is critical– The above 2 pieces of information are key to

determine Matrix trial arm eligibility

CR-UK NGS Panel 2

Nextera hybridisation 28 genes

Detects SNVs, indels, CNV, SV

Matched blood sample to subtract germline

variants

AKT1 ALK BRAF CCND1

CCND2 CCND3 CCNE1 CDK2

CDK4 CDKN2A EGFR FGFR2

FGFR3 Her2* HRAS KRAS

MET NF1 NRAS NTRK1

PIK3CA PTEN RB1 RET

ROS1 STK11 TSC1 TSC2

National Lung Matrix Trial Series of single arm phase II trial arms Testing experimental targeted drugs Population stratified by pre-specified putative actionable

biomarkers Bayesian adaptive umbrella design Arm for population with no actionable genetic changes

8 investigational medicinal products 21 patient cohorts

NSCLC histology Molecular status

https://www.birmingham.ac.uk/research/activity/mds/trials/crctu/trials/lung-matrix/professionals/index.aspx

Treatment arms – wild type IMPORTANTArm Medicinal

ProductCohort NSCLC

HistologyMolecular Status

C Palbociclib –CDK-4/6 Inhibitor

C1 SCC CDKN2A loss +Wild type RB

C2 ADD or NOSNSCLC

CDKN2A loss +Wild type RB

C3 NSCLC CDK4 amplification +Wild type RB

C4 NSCLC CCND1 amplification +Wild type RB

C5 NSCLC STK11 or TSC1/2 mutation +KRAS/NRAS/NF1 mutation +Wild type RB

C6 NSCLC KRAS mutation +Wild type RB, STK11, PIK3CA, PTEN, AKT, EGFR, FGFR2/3, TSC1/2, HER2

Genomics England - 100KGP Transformation of NHS

WGS can become a routine clinical investigation Aim to sequence 40,000 genomes from 20,000 cancer patients Paired blood and tumour

Variant subtraction Clinical interpretation

Enables genome wide inspection of somatic variants Mutation signatures

Tumour mutation burden Indicator for Immunotherapy

Tumour heterogeneity Multi-region sampling

Pertinent germline findings – reporting back

Actionable gene-based classification toward precision medicine in gastric cancer Ichikawa et al, Genome Medicine (2017) 9:93

Poor outcome for unresectable and recurrent cancers Intertumour heterogeneity significant hurdle to identifying optimised

targeted therapies in GC Current molecular classifications not associated with targeted therapies

Comprehensive genomic profiling in 207 gastric tumours

435 cancer genes69 FDA “actionable” genes

EBV infection &Microsatellite instability status

141/207 tumours (68%) = actionable gene alteration

N = 32Hypermutated

N = 25ERBB2

N = 10CDKN2A/B

N = 10KRAS

N = 9BRCA2

N = 12ATM

N = 109Minor/No alterations

Novel Classification

Each associated with an FDA approved targeted therapy (clinical trials needed to show effectiveness)

References