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Utsw 2020 1
- 1. On Prediction Guidance in Radiotherapy: From Experimental Scaffold to Clinical Implementation Mohamed E. Abazeed, MD, PhD Northwestern University
- 2. Cassandra Complex The Perils of Prediction Corso Complex
- 3. “With perfect knowledge, we ought to make perfect predictions”
- 4. Tumors Represent Complex Systems
- 5. Scaffolds are Not Pejorative: Intricate and Useful
- 6. Cancer Models Adv Drug Deliv Rev. 2014. 79-80:40-9.
- 7. An Integrated Clinomic Network
- 8. To develop an information capability, you first need information… I. >550 variants tested to date II. Systematic unary, massively parallel testing of variants III. Primary screen completed IV. Validation phase ongoing I. 533 cell lines II. Associations made with >65,000 genomic features III. Published in 2016 I. ~361 PDXs generated to date II. Project launched in 2019.
- 9. Center Structure and Division of Labor
- 10. CCLE is a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from >1000 human cancer cell lines. We use established and new algorithms to reflect omic data on our phenotype (survival after drug/radiation exposure) and identify biological predictors of response to therapy (resistance and sensitivity). CCLE Nature. 2012 Mar 28;483(7391):603-7. Multi-omic Databases
- 11. Nat Commun. 2016 Apr 25;7:11428. Predicting Response in a Probabilistic Landscape
- 12. Genetic Determinants Of Tumor Radiation Sensitivity *Five of the top six genes that when mutated correlate with sensitivity to IR have confirmed roles in the DNA damage response: FLNA, SMG1, TP53BP1, LRP1, and RIF1. **WES has been made publically available. * * * * *
- 13. Computational Pipeline for Nominating Variants
- 14. Variants Tested
- 15. Genetic Strategy for Testing Variants
- 17. Residue Number Integral Survival Too much noise at the gene level Residue Number Integral Survival Population AVG Gene AVG Gene X Population AVG Gene AVG Gene Y Stratify by Protein Domains: Enriching for Signal from the Noise
- 18. Genetic Variants that Regulate Survival after Irradiation Primary Screen: • >1600 replicates • 508 Variants • 92 genes
- 19. Relevance of Cellular Context -0.2 0.0 0.2 0.2 0.4 0.6 0.8 Beas2B TRT-HU1 BRAF WT BRAF G466V BRAF G469V FBXW7 WT FBXW7 V464E EGFR WT EGFR L858R EGFR T790M+L858R NRAS WT NRAS G12C NRAS G13R NRAS Q61K MAP2K1 WT MAP2K1 K57T MAP2K1 E203KPIK3CA WT PIK3CA E545K PIK3CA H1047R VHL WT VHL L158V VHL R161P CTNNB1 WT CTNNB1 A622S Nrf2 WT-1 Nrf2 WT-2 Nrf2 E79K
- 20. 0 0.02 0.04 0.06 0.08 0.1 0.12 in silico CMG Random HIT RATES The Utility of the Priors
- 21. Variants that Confer Radiation Resistance Are Evolutionarily Conserved & Are Under Somatic Oncogenic Selection
- 22. N TC 1 N TC 2 K EA P1 K O 1 K EA P1 K O 2 0.0 0.2 0.4 0.6 FractionViable 4 Gy mCherry Wild type E117K P278S G333C R470S KEAP1 wild type, but not DN or l-o-f variants, restores radiation sensitivity * * Dominant Negative Development of a Complementation Platform to Query DN v. l-o-f Variants KEAP1 GAPDH NRF2 HDAC1 Cytoplasmic Nuclear KEAP1 KEAP1 alleles GAPDH HDAC1 NRF2 Complementation with CRISPR-resistant cDNA KO2 Cells CRISPR-Cas9 targeted to an exon-intron junction
- 23. 0.0 0.5 0 200 400 600 Normalized AUC (Log2) ResidueNumber KEAP1 R470H R470S G430C P278S R272C E117K G333C KEAP1 WT 0.0 0.5 0 200 400 600 Normalized AUC (Log2) ResidueNumber KEAP1 (CFA) R470S P278S E117K G333C KEAP1 WT Validation by CFA
- 24. Validation in vivo & Variant Identity
- 25. Similar Genetic Dependencies Between IR and Some Drugs
- 26. A multitude of mutations lead to a limited number of functional outcomes
- 27. Next steps? -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 log2AUC NRF2: DLG ETGE NRF2 E79K +s -s +s -s KEAP1 MEK1: Kinase SMAD4: SAD, MH2 DRD2: Transmembrane CTNNB1: Armadillo ERBB3: Extracellular KRAS: GTP BRAF: Kinase ARAF: Kinase TET2: Gln Rich Pan-Cancer (other cellular contexts) Multi-Allele Interactions via multi-plex CRISPR Molecular Mechanisms Similarity Maps with Chemical Therapies Radiation Sensitivity Phenotypic Impact Profiling Validate New and Rare Variants
- 29. Saliva PDX Tumor Blood 0 100 200 300 400 500 600 HNSCC SCLC LUSC LUAD Misc. 34 12 56 78 0 112 67 145 212 56 239 87 273 452 105 257 157 357 578 213 Saliva PDX Tumor Blood Our Laboratory’s Biobank (living and frozen)
- 30. 500 PDXs/10,000 mice/Integrated Omic Analysis
- 31. PDXs are not supposed to be amenable to HTP
- 32. PDXs are not supposed to be amenable to HTP
- 33. I. Correlations with clinical outcomes. II. Clonal reconstructions of the primary and recurrent tumors. III. Sex specificity (crossover). IV. Time of day of treatment (cortisol release in mice). V. Orthotopic versus heterotopic. VI. Immune cell capture. VII. microCT (radiomic feature stability and genomic correlates—in vivo or ex vivo). Data Collection Variables
- 34. The Need for a Framework for Individualizing Radiation Dose J Thorac Oncol. 2017 Mar;12(3):510-519. Nat Commun. 2014 Jun 3;5:4006. https://doi.org/10.1016/S2589-7500(19)30058-5
- 35. We seek to augment intelligence, not replace it…
- 36. We seek to augment intelligence, not replace it… This could benefit from augmentation.
- 37. We seek to augment intelligence, not replace it…
- 38. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 39. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 40. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 41. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 42. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 43. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 44. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 45. An Image-based Framework for Individualizing Radiation Dose https://doi.org/10.1016/S2589-7500(19)30058-5
- 46. An Image-based Framework for Individualizing Radiation Dose
- 47. Can you associate the feature space with individual genetic alterations?
- 48. Concatenating Datasets via Transitive Learning
- 49. Did the work Titas Bera, BS Jessica Castrillon, MS Priyanka Gopal, MS Aaron Petty, MS Mishka Gidwani, MS Semihcan Doken, MS Alums Eui Kui Chie, MD, PhD (SNUH) Gwendolyn Kuzmishin, BS (CCLCM) Kevin Rogacki, MD (Northwestern) Craig Peacock, PhD (CWRU) Roberto Vargas, MD (CCF) Brian Yard, PhD (Viocera) NIH KL2CA188339-01 NCI R37CA222294-01 Acknowledgments Collaborators Drew Adams, PhD (CWRU) Pablo Tamayo, PhD (UCSD) Peter Hammerman (Novartis) Ben Haibe-Kains, PhD (UoT) Scott Bratman, PhD (UoT) Urs Hagemann (Bayer) Gerhard Siemeister (Bayer) Ali Kamen, PhD (Siemens) Bin Lou, PhD (Siemens)
Editor's Notes
- CADD scores are based on diverse genomic features derived from surrounding sequence context, gene model annotations, evolutionary constraint and functional predictions. It uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees. The differences between the de novo simulated variants and the actual observed variant that have been fixed represent the variants that have a high CADD score (Combined Annotation-Dependent Depletion).
- PDX models recapitulate the genetic complexity of human tumors and are therefore predicted to represent improved models for drug and predictive biomarker development (4). These patient derived models have the promise to serve as powerful tools to enhance clinical trial success at a key step in biomarker and drug development.