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CDAC 2018 Ciccolella inferring

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Presentation at the 2018 Workshop and School on Cancer Development and Complexity (CDAC 2018)

Published in: Science
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CDAC 2018 Ciccolella inferring

  1. 1. Inferring cancer progression from single cell sequencing while allowing loss of mutations Simone Ciccolella, Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha and Paola Bonizzoni Lake Como School of Advanced Studies, 2018
  2. 2. Introduction Simone Ciccolella CDAC 2018 • Cancer phylogeny • Mutation losses • Simulated Annealing Single Cell (SASC) inference tool • Experimental results
  3. 3. Cancer evolution Simone Ciccolella CDAC 2018 • Different clones make different fractions of the tumor • Accumulation of mutations over time • Being able to detect the evolutionary history of a tumor is a key stone for developing targeted therapies
  4. 4. Cancer evolution Simone Ciccolella CDAC 2018 • Different clones make different fractions of the tumor • Accumulation of mutations over time • Being able to detect the evolutionary history of a tumor is a key stone for developing targeted therapies Picture from: Ding et al., Nature, 2012.
  5. 5. Infinite Sites Assumption Simone Ciccolella CDAC 2018 • The most assumed assumption for the inference of cancer evolutions • Permits the use of the simplest phylogeny model • Easiest model from a computational perspective No two mutations can occur at the same locus (site). Kimura, Genetics, 1969.
  6. 6. Infinite Sites Assumption Simone Ciccolella CDAC 2018 • The most assumed assumption for the inference of cancer evolutions • Permits the use of the simplest phylogeny model • Easiest model from a computational perspective No two mutations can occur at the same locus (site). Kimura, Genetics, 1969. • “Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity for more effective cancer treatment.” From: Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors. Kuipers et al., Genome Research, 2017. • “In genomically unstable cancers, deletion of large chromosomal segments is common.” From: Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Brown et al., Nature 8, 2017.
  7. 7. Phylogenies: Perfect vs Dollo Simone Ciccolella CDAC 2018 Perfect Phylogeny BA FC D E Each mutation is acquired once in the evolutionary history H
  8. 8. Phylogenies: Perfect vs Dollo Simone Ciccolella CDAC 2018 Perfect Phylogeny Dollo(k ) Phylogeny BA FC D E Each mutation is acquired once in the evolutionary history BA FC D A1 – E B1 – G A2 – H I Each mutation is acquired once, but it can be lost at most k times in the evolutionary history H
  9. 9. Loss of a mutation Simone Ciccolella CDAC 2018 …… ……
  10. 10. Loss of a mutation Simone Ciccolella CDAC 2018 …… …… ……
  11. 11. Loss of a mutation Simone Ciccolella CDAC 2018
  12. 12. Loss of a mutation Simone Ciccolella CDAC 2018 …… ……
  13. 13. Loss of a mutation Simone Ciccolella CDAC 2018 …… …… ……
  14. 14. Loss of a mutation Simone Ciccolella CDAC 2018
  15. 15. Single Cell Sequencing Simone Ciccolella CDAC 2018
  16. 16. Available methods for SCS Simone Ciccolella CDAC 2018 SCITE [1]: • Markov Chain Monte Carlo (MCMC) maximum likelihood tree search • Relies on the Perfect Phylogeny model • Produces solutions with respect to the Infinite Site Assumption [1] Tree inference for single-cell data. Jahn K., Kuipers J. and Beerenwinkel N., Genome Biology, 2016.
  17. 17. Available methods for SCS Simone Ciccolella CDAC 2018 SCITE [1]: • Markov Chain Monte Carlo (MCMC) maximum likelihood tree search • Relies on the Perfect Phylogeny model • Produces solutions with respect to the Infinite Site Assumption SiFit [2]: • Hidden Markov Model (HMM) maximum likelihood tree search • Does not impose any specific phylogeny model • Can produce solutions that violate the Infinite Site Assumption [1] Tree inference for single-cell data. Jahn K., Kuipers J. and Beerenwinkel N., Genome Biology, 2016. [2] SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Zafar H., Tzen A., Navin N., Chen K. and Nakhleh L., Genome Biology, 2017.
  18. 18. SASC: Methods Simone Ciccolella CDAC 2018 SASC is a Simulated Annealing maximum likelihood tree search algorithm: • Optimization criteria: max 1 1 log ( 𝑃 𝐼89 𝐸89 ) 98 • A heuristic method for approximating the global optimum of a given function in large search space. • The algorithm moves from one solution to a new one with a set of predefined moves. • If the new solution is better, it will accepted with probability 1, otherwise the acceptance probability change w.r.t. the temperature: 𝑝 = 𝑒 @AB C@ADEF@G H GIJ C@ADEF@G EIKLIMNEDMI
  19. 19. SASC: Moves – Subtree Prune and Regraft Simone Ciccolella CDAC 2018 A B C D E F G HI PruneRegraft
  20. 20. SASC: Moves – Subtree Prune and Regraft Simone Ciccolella CDAC 2018 A B C D E F G HI PruneRegraft A B C D E F G H I
  21. 21. SASC: Moves – Swap nodes labels Simone Ciccolella CDAC 2018 A B C D E F G H I
  22. 22. SASC: Moves – Swap nodes labels Simone Ciccolella CDAC 2018 A F C D E B G H I A B C D E F G H I
  23. 23. SASC: Moves – Add a deletion Simone Ciccolella CDAC 2018 A F C D E B G H I
  24. 24. SASC: Moves – Add a deletion Simone Ciccolella CDAC 2018 A F C D E B G H I A F C D E I B G H F1 –
  25. 25. SASC: Moves – Remove a deletion Simone Ciccolella CDAC 2018 A F C D E B G H F1 –I
  26. 26. SASC: Moves – Remove a deletion Simone Ciccolella CDAC 2018 A F C D E B G H F1 –I A F C D E I B G H
  27. 27. SASC: Assignment of cells Simone Ciccolella CDAC 2018 A B C D E F G H I
  28. 28. SASC: Assignment of cells Simone Ciccolella CDAC 2018 A B C D E F G H I cell6 cell1 cell2 cell3 cell4 cell5 cell7cell8
  29. 29. Results: Simulated data Simone Ciccolella CDAC 2018 • Ancestor-Descendant accuracy: Pairs of mutations in Ancestor-Descendant relationship correctly inferred • Different lineages accuracy: Pairs of mutations in different branches correctly inferred F1 scores
  30. 30. Results: Simulated data Simone Ciccolella CDAC 2018 • Accuracy of deletions detection: Classification accuracy of mutational losses (*) SCITE and SiFit detect no deletion
  31. 31. Results: Real data Simone Ciccolella CDAC 2018 Data from: Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Gawad et al., Proceedings of the National Academy of Sciences, 2014. Lymphoblastic Leukemia Breast Cancer Data from: Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Chung et al., Nature Communications, 2017. Bold-faced mutations are driver mutations
  32. 32. Conclusions Simone Ciccolella CDAC 2018 • SASC is an accurate tool for inferring intra-tumor progression and subclonal composition from SCS data • SASC is highly accurate on both simulated and real datasets • SASC infers mutation losses employing a Dollo model • SASC provides a new progression model on Single Cell data • Future directions: 1. Explore different heuristics (Genetic Programming) 2. Define new methods that reduce the dimensions and complexity of the search space 3. Find a better name for the tool
  33. 33. Thank you

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