translating ngs data into a clinically actionable assay n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Translating NGS Data into a Clinically Actionable Assay PowerPoint Presentation
Download Presentation
Translating NGS Data into a Clinically Actionable Assay

Loading in 2 Seconds...

play fullscreen
1 / 41

Translating NGS Data into a Clinically Actionable Assay - PowerPoint PPT Presentation


  • 143 Views
  • Uploaded on

Translating NGS Data into a Clinically Actionable Assay. Elaine R. Mardis, Ph.D. Professor of Genetics Co-director, The Genome Institute. NCI Workshop: NGS in Clinical Decision Making. Why is cancer WGS analysis “easy”?. The comparison of a patient’s tumor to their normal genome

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

Translating NGS Data into a Clinically Actionable Assay


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
    Presentation Transcript
    1. Translating NGS Data into a Clinically Actionable Assay Elaine R. Mardis, Ph.D. Professor of Genetics Co-director, The Genome Institute NCI Workshop: NGS in Clinical Decision Making

    2. Why is cancer WGS analysis “easy”? • The comparison of a patient’s tumor to their normal genome • Provides an individualized comparison of what is truly somatic vs. what is truly inherited (germline) • Existence of online information about frequently mutated genes in cancer samples (COSMIC) • Large-scale efforts using NGS methods to catalogue mutated genes (e.g. TCGA)

    3. Why is cancer genome analysis challenging? • In solid tumors, there are normal cells present to differing degrees. Certain tumor types are quite diffuse (prostate, pancreas) and may require specific tumor cell isolation by LCM or flow sorting • Conventional pathology may require the majority of the tumor block, leaving little for genomics (melanoma) • FFPE preparation from pathology (DNA/RNA degradation) • Genomic aneuploidy and amplification of chromosomal segments impacts the coverage model • Cellular heterogeneity is a reality (not all cells contain all mutations) • In blood or “liquid” tumors, a skin biopsy is taken for the normal but may contain high circulating tumor cell counts at diagnosis

    4. The human genome reference sequence is the keystone for cancer genome sequence analysis. Tumor and normal genomes are compared separately to the human reference sequence, then to one another, to identify somatic variation of all types. Mis-aligning sequences identify structural alterations. Whole Genome Sequencing Process SNP Typing of Tumor and Normal gDNA Shotgun library construction Sequence data generation Human reference alignment Computational detection of somatic changes

    5. Somatic Point Mutation Discovery SNVs indentified in Tumor Somatic SNiPer +Normal sequence alignments Predicted Tumor-unique SNVs • Strand bias (via binomial test) • Distance to effective 3’ end of read (via K-S test) • Paralog filter (via sum of mismatch base qualities) • Homopolymer filter (number of consecutive bases • preceding or following the variant) Subtract dbSNP Candidate Patient Tumor-unique SNVs Tier 4: The rest Tier 1: Coding NS SNVs Splice site SNVs Coding SS SNVs SNVs in RNA genes Tier 2: SNVs in highly conserved blocks SNVs in regulatory regions Tier 3: SNVs in Non-repetitive regions

    6. WUGC Somatic SV/CNV Pipeline Validation data go through parts of this pipeline

    7. Custom Capture for Validation and Read Depth gDNA Illumina library Hybridization Custom capture probes (target each variant site) Bind to Streptavidin Magnetic Beads Sequence Variant Sites and SV assemblies at ~1000-fold Depth

    8. “AML1”: Cancer Genomics by Whole Genome Sequencing Cancer Genomics • Caucasian female, mid-50s at diagnosis • De novo M1 AML • Family history of AML and lymphoma • Informed consent for whole genome sequencing • Solexa sequencer, 32 bp unpaired reads • 10 somatic mutations detected • Ley et al., Nature 2008 R.K.Wilson2011

    9. Tumor Sequencing is Driving Discovery Total WGS samples: 1351 Pediatric and adult tumors with comprehensive clinical data to address clinically relevant questions

    10. Every cancer is different…

    11. Conclusion: “…whole genome characterization will become a routine part of cancer pathology.”

    12. Cancer Genomics in the Clinic Therapeutic Options via NGS

    13. tAML Case Presentation • 37 y.o. female presented with T2N1 Breast CA ER/PR/Her2+. BRCA1/2-normal. • At age 39-Stage III-C ovarian CA diagnosed. • At age 43-locally recurrent ovarian CA. • 2 months after completing chemotherapy, presented with t-AML/respiratory failure. Expired 9 days after presentation. • Detailed family history did not suggest inherited cancer susceptibility. Patient has three minor children. Link et al., JAMA 2011; 305(15): 1568-76

    14. tAML Spectral karyotype 46,XX,der(3)ins(3;4)(q26.2;q13.3q31.1)ins(3;3)(q26.2;q27q12)t(3;4)(q26.2;p12),der(3)ins(3;3)(q26.2;q27q12),der(4)ins(3;4)(q26.2;q13.3q31.1)t(3;4)(q26.2;p12),der(5)del(5)(q13.3q34)t(5;12)(q34;p12.3),r(7)(p11.2q11.2),der(12)t(5;12)(q34;p12.3)[14]/ 45,idem,-r(7)(p11.2q11.2)[9]

    15. Whole genome analysis indicates the patient has Lei-Fraumeni syndrome. Previously undetected by clinical assay due to nature of the 3 exon deletion. Genomic data are supported by RNA analysis. tAML: TP53 germline deletion

    16. Clinical case: atypical APL 37 y.o. female with de novo AML; M3 morphology Chemo + ATRA Complex cytogenetics, persistent leukemia Chemo only First remission, referred to WU for SCT. rBM: normal morphology, cytogenetics; negative for PML/RARA. ??? Allogeneic SCT Consolidation + ATRA

    17. Welch et al., JAMA 2011: 305(15): 1577-1584.

    18. “Genome-Guided Medicine”: An early example Detection of PML-RARA by WGS, Confirmed by FISH, RT-PCR (CLIA/CAP) Consolidation: Chemo + ATRA 37 y.o. female with de novo AML, M3 morphology, CTG, no PML-RARA. Referred to WUSM for SCT. Sustained remission

    19. Cancer Genomics in the Clinic Therapeutic Options via “Gx,Ex,Tx”

    20. NGS “Diagnostic Trials: An N of 1” • Cancer patients consented for genomic sequencing and return of information • Cancer biopsies studied by WGS, exome and transcriptome integrated analysis • WGS drives discovery • Exome contributes read depth for heterogeneity/clonality analysis • Transcriptome monitors aberrant gene expression and validates fusions • Interpretive analysis should accurately identify actionable targets and available clinical trials. • All possibly actionable mutations/alterations are verified in CLIA lab with pathology sign-off. • A Tumor Board model for education, decision-making, and patient monitoring is critical. Sharing results to the community is desired/critical!

    21. Clinical Genome Analysis Pipeline Somatic/Germline Cancer Events (DNA+RNA) TGI Drug-Gene interaction database (24 database sources) Clinical prioritization and reporting SNVs Kinases Indels Functional annotation RTKs DrugBank SVs TTD Filtered (activating/drivers) CNVs clinicaltrials.gov Fusions PharmGKB Candidate genes/pathways DE genes STICH2 Etc … DE isoforms Clinically actionable events

    22. Combined exome capture and in silicoepitope prediction in a chemically-induced mouse sarcoma model We identified a highly immunogenic tumor-specific mutated protein antigen that targets tumor cells for elimination in an immune-capable host. First demonstration using genomics to identify a tumor antigen from an unedited tumor, and to demonstrate that T-cell-dependent immunoselection is a mechanism underlying the outgrowth of tumor cells that lack a strong rejection antigen(s). Tumor Immunoediting : Somatic mutations as vaccine targets

    23. Examples of Diagnostic Sequencing Metastatic breast cancer

    24. HG1 Patient History • Female patient, mid-50’s with history of DCIS and Paget’s disease of the left nipple 2007 • Widespread metastatic breast cancer to bone 2009, biopsy shows ER- HER2+ disease (FISH amplified), highly responsive to paclitaxel + trastuzumab • Brain metastasis in posterior fossa diagnosed May 2010, treated with surgery (sample for sequencing obtained) radiosurgery and lapatinib • Progressive disease in March 2011: treated with further surgery and whole brain irradiation • July 2011: systemic disease still under control with trastuzumab in combination with lapatinib

    25. Somatic mutation frequencies hint at heterogeneity 92 point mutations are identified in genes Tumor variant allele frequency Proportion Read coverage (X) Tumor variant allele frequency Tumor variant allele frequency Metastatic breast cancer (to brain)

    26. Somatic copy number variants – genome wide Metastatic breast cancer (to brain)

    27. Somatic copy number variants – chromosome 17 HER2 HER2 / ERBB2 is heavily amplified in this tumor Metastatic breast cancer (to brain)

    28. RNA-seq confirms the HER2, PR, & ER status HER2 +ive • Gene expression values from RNA-seq • Used to confirm HER2, PR, & ER status of each patient • Tumor is • HER2+, PR-, ER- ER- PR- Metastatic breast cancer (to brain) vs. four primary HER2 –ve breast cancers

    29. Somatic copy number variants – chromosome 6 HDAC2 (histone deacetylase 2) is amplified to almost the same degree as HER2 Metastatic breast cancer (to brain)

    30. RNA expression pattern confirms HDAC2 over-expression. Patient predicted to respond to the HDAC2 inhibitor Vorinostat [DrugBank]. RNA expression – HDAC2 HDAC2 genomic amplification is accompanied by high RNA expression Metastatic breast cancer (to brain) vs. four primary HER2 –ve breast cancers

    31. PNC-2 Tumor: Pancreatic Neuroendocrine Metastatic Disease • Initial diagnosis: Pancreatic Neuroendocrine tumor • First metastatic tumor (liver) banked in 2005 (FFPE), no adjuvant chemotherapy • Second metastatic tumor to liver banked in 2011(FFPE), following neoadjuvant chemotherapy, including everolimus + Bevacizumab • Patient consented for return of results from whole genome sequencing • We produced WGS and exome capture data from the two metastatic tumors and a blood normal. RNA-seq from both metastatic tumors.

    32. PNC2: Comparing metastatic tumor presentations Met1 Clonality Met2 Clonality Although 33 mutations were identified in the tumor genome, none were considered druggable…

    33. Based on our RNA-seq analysis, VEGFA is increasing in its expression levels from the initial metastatic lesion sampled in 2005, to the present lesion sampled in 2011. The DrugBank prediction for VEGFA overexpression is treatment with Bevacizumab/Avastin. PNC2: RNA-seq analysis

    34. As happens in sequencing advanced metastatic patients, this patient died before being treated based on the genomic predictions. However, post-mortem consultation with the patient’s oncologist indicated that perfusion CT during bevacizumab treatment showed response was evident. However, Bevacizumab had been withdrawn due to side effects. PNC2: “Post-mortem” Diagnostics Baseline 3 weeks on Everolimus 6 weeks after adding Bevacizumab

    35. Example case Acute lymphocytic leukemia

    36. Case study: 2nd relapse B-ALL • Age 25: Initial presentation of classic pre B-ALL Standard induction, consolidation, and 2 years of maintenance therapy Marrow banked • Age 30: 1st relapse CR obtained with salvage chemo consolidation with a matched sibling allo transplant very mild GvHD • Age 33: 2nd relapse, CNS involvement (July 2011) During induction chemotherapy, we sequenced T/N genomes using banked blasts from initial presentation, exomes (T/N) and RNA-seq of blasts

    37. ALL-1: Somatic single nucleotide variations • 91 somatic coding SNVs • 42 with evidence for expression in RNA-seq Although 91 mutations were identified in the tumor genome, none were considered druggable…

    38. ALL-1: Tumor heterogeneity Tumor variant allele frequency Proportion NF1 2,074 tier1-3 somatic variants. 91 are tier1 (coding exons) Read coverage (X) Tumor variant allele frequency Tumor variant allele frequency Acute lymphocytic leukemia

    39. Patient’s activated FLT3 gene was targeted with sunitinib, complete clinical remission was achieved in 12 days, enabling MUD SCT. Identification of discrete chromosomal deletions in tumor cells provides a means for ongoing tumor assessment with interphase FISH (presence of MRD) Four months post-SCT, the patient is back at work. CD135 (FLT3) added to the flow panel for all B-ALL patients at Barnes-Jewish Hospital. ALL1: RNA-seq analysis ALL1 Pre-B-ALL Naive B-cells

    40. Summary • We can produce comprehensive whole genome analysis of cancer patients now, and the data can provide very important input for clinical, therapeutic decision making. • Not all patients will benefit, however, because of our current knowledge gaps and because targeted therapies are not yet available for many important cancer genes. • Many regulatory issues must be resolved before these tools can be used widely. Discussions are ongoing at NIST, FDA, CAP etc. • Each case represents a focused effort involving genomicists, oncologists, pharmacologists and pathologists (at least). Physician education in genomic data interpretation is a tangential benefit. • Off label use of therapies may become common. Sharing results is a critical exercise at sites implementing this approach.

    41. Acknowledgements The Genome Institute Li Ding, Ph.D. Malachi Griffith, Ph.D. David Dooling, Ph.D. David Larson, Ph.D. Nathan Dees, Ph.D. Vincent Magrini, Ph.D. Sean McGrath Jason Walker Amy Ly Daniel Koboldt Lucinda Fulton Robert Fulton Lisa Cook Ryan Demeter Todd Wylie Kim Delehaunty Michael McLellan Rick Wilson WUSM/Siteman Cancer Center Timothy Ley, M.D. Matthew Ellis, M.B., Ph.D. Benjamin Tan, M.D. John DiPersio, M.D., Ph.D. Timothy Graubert, M.D. Matthew Walter, M.D. John Welch, M.D., Ph.D. Jackie Payton, M.D., Ph.D. Peter Westervelt, M.D., Ph.D. Lukas Wartman, M.D. Our patients NHGRI NCI WUCGI