Aug2015 deanna church analytical validation
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Aug2015 deanna church analytical validation
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Aug2015 deanna church analytical validation
- 1. © 2014 Personalis, Inc. All rights reserved. Pioneering Genome-Guided Medicine Perspectives on analytical validity Deanna M. Church, PhD Senior Director of Genomics and Content Personalis, Inc
- 2. 2 Disclosure I work for Personalis, Inc. A company that provides whole genome and augmented whole exome sequencing, analysis services and clinical interpretation services.
- 3. 3 • What RMs would be useful for analytic validation of somatic variants? • How does targeted sequencing differ from WGS in terms of analytic validation needs? • What’s the role of benchmarking data sets in validating bioinformatics? • Is there a role for “benchmark” or “reference” pipelines? • What GIAB products other than RMs should we produce? – Would a product like a whitepaper outlining the common pitfalls in analytic validation for NGS be a good product? – Is there a need for other process controls (e.g., FFPE-embedded, mixtures, etc.)? – What role can spike-ins play in validation? What would they look like? For somatic mutations? For germline mutations? • What are the most specific knowledge gaps in how to do analytic validation for NGS?
- 4. © 2014 Personalis, Inc. All rights reserved. ACE Clinical Exome™ with Enhanced Diagnostic Yield Assay development and evaluation
- 5. 5 Deficits in Coverage in a Key Gene Variants in RPGR cause ~80% of X-linked retinitis pigmentosa Previously described variants Depth Coverage Plot of RPGR Dark blue represents coverage at 1 standard deviation from mean >20x Coverage (required to call heterozygous SNVs and indels accurately) * Coverage plots are representative sequence coverage based upon N=16
- 6. 6 >20x Coverage (required to call heterozygous SNVs and indels accurately) Previously described variants Depth Coverage Plot of RPGR Dark blue represents coverage at 1 standard deviation from mean * Coverage plots are representative sequence coverage based upon N=16 Assay improvement Variants in RPGR cause ~80% of X-linked retinitis pigmentosa Enhanced Exome Standard Exome p.Glu809Glyfs*25
- 7. 7 Coverage in medically interpretable genes Percent bases with >20X local high quality coverage depth: finishing metric Percentfinishedexons coding non-coding Augmented exome Exome 1 Exome 2 Exome 3 Exome 4 31X PCR-free WGS Patwardhan et al., Genome Medicine 2015
- 8. 8 Identifying low frequency alleles ACE Exome 12G ACE Cancer Panel 12G WGS 100G (30x) TP53
- 9. 9 Breakdown • How does targeted sequencing differ from WGS in terms of analytic validation needs? Targeted sequencing is an important part of improved performance. The analytical needs are similar, but the reference data must contain variants in all parts of the genome, even the hard ones.
- 10. 10 Breakdown • Is there a role for “benchmark” or “reference” pipelines? This is really of limited utility as custom assay development often has custom informatics.
- 11. 11 Breakdown • What are the most specific knowledge gaps in how to do analytic validation for NGS? Increased transparency on exact intervals being tested and metrics based on variant type and allelic fraction.
- 12. © 2014 Personalis, Inc. All rights reserved. ACE Clinical Exome™ with Enhanced Diagnostic Yield Cancer clinical validation study
- 13. 13 ACE Cancer Panel CLIA Validation Results ACE Cancer Panel Performance Specifications Sensitivity Base Substitutions >99% MAF ≥ 5% Indels >99% MAF ≥ 10% CNAs 97% tumor content ≥ 20% Gene Fusions >99% Specificity >99%* Typical Median Depth >500X Sample Types Fresh Frozen or FFPE Tumor Samples ≥ 20% Tumor * Based on Base substitutions and Indels, others pending
- 14. 14 Breakdown • What are the most specific knowledge gaps in how to do analytic validation for NGS? Increased transparency on exact intervals being tested and metrics based on variant type and allelic fraction.
- 15. © 2014 Personalis, Inc. All rights reserved. ACE Clinical Exome™ with Enhanced Diagnostic Yield Real life samples
- 16. 16 FFPE sample challenge Large range of performance for FFPE samples 40 50 60 70 80 90 100 MCC437_DNA MCC438_DNA DNASEQ2_tum MTSCC_DNA SAM20370921 5 S06_41775_A1 4 A02-21A S09_3537_A2 C13-2710A1 ONYX8878 JK-3 SAM20370921 PharmB S04_42981_A1 S14_17554_B2 PharmA 03-22957A1 PS13_9876_A8 PR13_269_52A GLUT1_Normox S06_21760_A2 SAM20370937 B1 PR13_269_13A JK-9 PR13_269_31A JK-11 05-29776 3467B_DNA 14-02182A16 SAM20370920 13 JK-8 1400203252 1326006032 SAM20370924 AZ_DNA_1 AZ_DNA_3 1106-091-201B- AZ_DNA_10 AZ_DNA_7 SAM20370932 AZ_DNA_8 % mapped % mapped 20 30 40 50 60 MCC437_ MCC438_ DNASEQ2 MTSCC_D SAM2037 5 S06_4177 4 A02-21A S09_3537 C13-2710 ONYX887 JK-3 SAM2037 PharmB S04_4298 S14_1755 PharmA 03-22957 PS13_987 PR13_269 GLUT1_N S06_2176 SAM2037 B1 PR13_269 JK-9 PR13_269 JK-11 05-29776 3467B_D 14-02182 SAM2037 13 JK-8 14002032 13260060 SAM2037 AZ_DNA_ AZ_DNA_ 1106-091- AZ_DNA_ AZ_DNA_ SAM2037 AZ_DNA_ Qmap Qmap
- 17. 17 RT 37C 45C 3 day 1 day 3 day 1 day 3 day 1 day 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 61 2 3 4 5 6 1 2 3 4 5 6 1. 0% PBS 2. 20% PBS 3. 40% PBS 4. 60% PBS 5. 80% PBS 6. 100% PBS Genomic DNA Extracted from FFPE samples
- 18. 18 RT 37C 45C 3 day 1 day 3 day 1 day 3 day 1 day 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 61 2 3 4 5 61 2 3 4 5 6 1 2 3 4 5 6 1. 0% PBS 2. 20% PBS 3. 40% PBS 4. 60% PBS 5. 80% PBS 6. 100% PBS DNA Library Generated from FFPE samples
- 19. 19 Breakdown • What RMs would be useful for analytic validation of somatic variants? More RMs that represent the variable sample quality seen in real clinical specimens.