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CDAC 2018 Elemento A precision medicine

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Presentation at the CDAC 2018 Workshop and School on Cancer Development and Complexity
http://cdac2018.lakecomoschool.org

Published in: Health & Medicine
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CDAC 2018 Elemento A precision medicine

  1. 1. A cancer precision medicine program driven by multi-omic profiling, analytics and modeling Olivier Elemento, PhD Director, Englander Institute for Precision Medicine
  2. 2. Talk Overview • The cancer precision medicine program at Weill Cornell – what have we learnt ? • Key challenges and how we can address them - How do we identify what treatments are most effective in individual patients? Patient-specific avatars - Can we rationally identify effective combination therapies ? Modeling complex pathways - How do we improve our understanding of disease biology ? Integrative and single cell biology • The need to meld analytics and experimentation
  3. 3. Precision Medicine at Weill Cornell Advanced cancer patient Highly personalized treatment recommendation Clinical sample Profiling Interpretation Recommendation
  4. 4. Return of results requires clinical assays: Cornell CLIA-approved whole-exome sequencing test queries >21,000 genes Rennert et al, 2016
  5. 5. How we report results matters – precision medicine reports
  6. 6. Cataloguing Clinically Relevant Mutations The Precision Medicine Knowledge Base (PMKB)  Genes  Variants  Interpretations  Tumor Types  Tissue Types https://pmkb.weill.cornell.edu/ Huang et al, 2016
  7. 7. Beltran et al, 2015; Rennert et al, 2016; Pauli et al, 2017 >1,500 cancer patients sequenced so far
  8. 8. What have we learnt ?
  9. 9. Revealed: Divergent evolution of metastatic tumors Faltas et al, 2016
  10. 10. Sailer and Beltran, in preparation Revealed: High prevalence of germline DNA repair alterations in metastatic cancer patients
  11. 11. Revealed: Transcriptomic reports help interpret genomic alterations
  12. 12. After Herceptin treatment Unexpected HER2 amplification in a bladder cancer patient leads to complete response
  13. 13. Pauli et al, 2017 Currently actionable mutations are not as frequent as we would like
  14. 14. Key challenges - How do we identify what treatments are most effective in individual patients? - Can we rationally identify effective combination therapies ? - How do we improve our understanding of disease biology and improve actionability ?
  15. 15. Leveraging patient specific avatars to identify what treatments are most effective in individual patients
  16. 16. Patient Avatars for Clinical Drug Screens
  17. 17. Example of organoids utility in drug testing: In vitro – Mini Sarcoma Production Pauli et al. , 2017
  18. 18. Pauli et al, 2017 Tumor Organoid Biobank Total Organoids in Biobank: 56 0 1 2 3 4 5 6 7 8 Cases PDX Establishment from Tumor Organoids PDX Established PDX Failed 7/7 3/3 1/2 1/1 2/2 0/1 2/2 1/1 2/3 0 10 20 30 40 50 60 Biopsies Tumor Organoid Establishment from Fresh Tissue Tumor Organoids Established Tumor submitted for Organoids 10/52 8/24 5/7 1/1 2/21/2 2/3 6/10 8/10 4/6 1/6 5/9 3/6
  19. 19. Single Agent Screen Results
  20. 20. Secondary Screen Results Pauli et al, 2017
  21. 21. Expected and new correlations between genomics and high-throughput drug screening
  22. 22. Building “vascularized” tumor organoids With Shahin Rafii
  23. 23. Xenograft 2 PID3 Zebrafish tumor xenograft/organoids Zebrafish embryos With Yariv Houvras
  24. 24. Can we more rationally identify effective combination therapies ? Individual molecules effective at killing some lymphoma cells There are tens of millions of possible combinations of 2, 3, 4, etc drugs !!!
  25. 25. What if we could create virtual disease models of cancer cells to test combinations in silico ? Proliferation Lymphomas are addicted to the BCR pathway
  26. 26. Du et al, 2017 Virtual disease model recapitulates known signaling data well
  27. 27. Virtual disease model predicts synergistic and antagonistic drug combinations Predictions Experiments
  28. 28. How do we improve our understanding of disease biology and improve actionability ?
  29. 29. Junttila et al, 2013 Complexity of the tumor micro-environment
  30. 30. Science May 2016 The cancer immune landscape – an example of complex process
  31. 31. 0 5000 10000 15000 20000 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 5000 10000 15000 20000 Freq JURKAT 0 10 20 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 5 10 15 20 25 Freq PM1026_Glioblastoma 0 5 10 15 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 5 10 15 Freq PM1032_Low_grade_neuroepithelial_neoplasm 0 100 200 300 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 100 200 Freq PM1041_Clear_Cell_Renal_Carcinoma 0 200 400 600 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 200 400 600 Freq PM1042_Squamous_Cell_Carcinoma_Lung 0 5 10 15 20 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 5 10 15 20 Freq PM1058_Medulloblastoma 0 1 2 3 4 TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9 Gene Frequency 0 1 2 3 4 Freq PM856_Malignant_neuroepithelial_neoplasm trb V−usage by count TCR V beta usage by count We can quantify the diversity of immune cells within tumors – example of TCR-seq
  32. 32. Predicting MHC presented neo- epitopes in tumors
  33. 33. New improved approaches for immune deconvolution from bulk RNAseq Also – CIBERSORT (Newman et al, 2016) Davide Risso
  34. 34. The Immune Response Index integrates the immune landscape to predict immunotherapy responders Machine learning (random forest) using clinical outcome data Bhinder et al, 2017; In preparation Independent test set
  35. 35. Junttila et al, 2013 Tumor Microenvironment, Single cell analysis and imaging of tumors
  36. 36. What disease really looks like at single cell resolution B cell lymphoma • How do cell population correlate with outcomes ? • How do cells communicate and can cross- talks be disrupted
  37. 37. Choi et al, 2015; Durrans et al, 2015 New paracrine crosstalk between macrophage IL6 and tumor IL6R
  38. 38. Conclusions • Patient-specific avatars enable mini n=1 clinical trials but also iterative learning • New technologies especially single cell technologies allow unprecedented understanding of the disease • Disease is complex – requires modeling and integrative analysis • Experimentation/measurements and analytics need to be closely integrated

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