Dr Yvonne Wallis Consultant Clinical Scientist West Midlands Regional Genetics Laboratory

Transcription

1 Dr Yvonne Wallis Consultant Clinical Scientist West Midlands Regional Genetics Laboratory

2 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 Cancer Clinical Pathway Diagnosis Treatment Prognosis Monitoring

3 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

4 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

5 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)

6 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 Pyrosequencing Multiple Target Test Next Generation Sequencing ddpcr Sanger sequencing FISH

7 Challenges - Heterogeneity Tissue heterogeneity Normal and tumour cell types Background wild type DNA Mask tumour mutations Macrodissection to enrich for tumour 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

8 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

9 Challenges - FFPE DNA QC Quantity dsdna Fluorometry Qubit/Picogreen Quality Fragmentation Bioanalyzer, tapestation Amplifiability - qpcr Contaminants UV absorbance Nanodrop qpcr

10 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 sequencing Genome wide visualisation of somatic variation

11 Challenges - Interpretation Standardisation of interpretation & reporting Four tiered system to categorise somatic variants Based on impact on clinical care Predicts sensitivity/resistance to therapy Supports inclusion in a clinical trial 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

12 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

13 Basic Steps of NGS: DNA to Data 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 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

14 NGS strategies Strategy: Panel Test Whole Exome Whole Genome Target: SELECTED genes All CODING genes WHOLE DNA sequence Size: Coverage: genes Variable size >99% target coverage ~20,000 genes 30 million bases Whole genome 3000 million bases >98% coverage >95% coverage Depth/Sensitivity: Throughput: Data storage: Cost: Variants: Gene related variants 30,ooo variants 3 million variants Interpretation: Actionable mutations Novel variants Incidental findings Novel variants Incidental findings

15 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/ % 1% 1/ % 2% 1/ % 4% 1/ % 5% 1/ % 10% 1/ Read depth Tumour %

16 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.

17 Phase 1 (SMP1) Sep 2011 Aug 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

18 The SMP1 Network: 3 Technology Hubs, 8 Clinical Hubs, 26 Feeder Hospitals Stratified Medicine Programme Cardiff Birmingham RMH Cardiff Manchester Glasgow Birmingham Edinburgh Cambridge RMH Leeds University Hospital of Wales The Christie Royal Infirmary University Hospital Western General Addenbrooke's Marsden St. James's Morriston Wythenshawe Western Infirmary City Royal Infirmary Papworth Royal Brompton General Infirmary Singleton Royal Gwent Velindre Salford Royal Pennine Acute Southern General Golden Jubilee Gartnavel Victoria Infirmary Infrastructure versus population density Technology Hubs Clinical Hubs Feeder Hospitals Stobhill Hospital

19 SMP2 Structure

20 SMP2 NGS Test CR-UK NGS Panel 2 Nextera hybridisation 28 genes Detects SNVs, indels, CNV, SV Matched blood sample to subtract germline variants 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 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

21 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

22 Treatment arms wild type IMPORTANT Arm Medicinal Product C Palbociclib CDK-4/6 Inhibitor Cohort NSCLC Histology Molecular Status C1 SCC CDKN2A loss + Wild type RB C2 ADD or NOS NSCLC 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

23 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

24 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 genes 69 FDA actionable genes EBV infection & Microsatellite instability status 141/207 tumours (68%) = actionable gene alteration Novel Classification N = 32 Hypermutated N = 25 ERBB2 N = 10 CDKN2A/B N = 10 KRAS N = 9 BRCA2 N = 12 ATM Each associated with an FDA approved targeted therapy (clinical trials needed to show effectiveness) N = 109 Minor/No alterations

25 References