Recent studies exploring the impact of methylation in tumor evolution suggest that while the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Since changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor’s single-cell methylation lineage tree and jointly identifying lineage-informative CpG sites which harbor changes in methylation status that are retained along the lineage. We apply Sgootr on the single-cell bisulfite-treated whole genome sequencing data of multiregionally-sampled tumor cells from 9 metastatic colorectal cancer patients made available by Bian et al., as well as multiregionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient made available by Chaligne et al.. We demonstrate that the tumor lineages constructed reveal a simple model underlying colorectal tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and more in concordance with the sequential-progression model of tumor evolution, in time a fraction of that used in prior studies. Interestingly, lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to CGI’s, which have been the main regions of interest in genomic methylation-related analyses. Sgootr is implemented as a Snakemake workflow, available at https://github.com/algo-cancer/Sgootr.

Artificial Intelligence (AI) is one of the most promising technologies of the 21. century, with an already noticeable impact on society and the economy. With this work, we provide a short overview of global trends, applications in industry and selected use-cases from our international experience and work in industry and academia. The goal is to present global and regional positive practices and provide an informed opinion on the realistic goals and opportunities for positioning B&H on the global AI scene.

Emerging sets of single-cell sequencing data makes it appealing to apply existing tumor phylogeny reconstruction methods to analyze associated intratumor heterogeneity. Unfortunately, tumor phylogeny inference is an NP-hard problem and existing principled methods typically fail to scale up to handle thousands of cells and mutations observed in emerging single-cell data sets. Even though there are greedy heuristics to build hierarchical clustering of cells and mutations, they suffer from well-documented issues in accuracy. Additionally even when “optimal” solutions are feasible, existing approaches only provide a single “most likely” tree to depict the evolutionary processes that may result in an observed collection of cells and mutations. To make matters worse, the vast majority of single-cell sequencing data sets are transcriptomic and as a result, suffer from considerable variation in coverage across mutational loci. In this paper, we introduce Trisicell, a computational toolkit for scalable tumor phylogeny reconstruction and validation from single-cell genomic, exomic or transcriptomic sequencing data. Trisicell has three components: (i) Trisicell-DnC, a new tumor phylogeny reconstruction method from genotype matrices derived from single-cell data, (ii) Trisicell-ConT a new algorithm for constructing the consensus for two or more tumor phylogenies - which may be built through the use of different data types on the same set of cells, or built through the use of different methods on the same data, and (iii) Trisicell-PF, a new partition function method for assessing the likelihood of any user-defined subtree/set of cells to be seeded by a given set of mutations in the phylogeny. Collectively, these tools provide means of identifying and validating robust portions of a tumor phylogeny, offering the ability to focus on the most important (sub)clones and the genomic alterations that seed the associated clonal expansion. We applied Trisicell to a panel of clonal sublines derived from single-cells of a parental mouse melanoma model on which we performed both whole exome and whole transcriptome sequencing. The tumor phylogenies of the clonal sublines built on exomic and transcriptomic mutations by Trisicell-DnC, were shown by Trisicell-ConT to be highly similar and the subtrees comprised of phenotypically similar clonal sublines were shown to be strongly associated by Trisicell-PF to their seeding mutations. In addition, we applied Trisicell to single-cell whole transcriptome sequencing data from a tumor derived from the same parental melanoma cell line, which was subjected to anti-CTLA-4 immunotherapy. The phylogenies generated from both studies featured distinct subtrees, strongly associated with phenotypes including cell differentiation status, tumor growth and therapeutic response. These results suggest that Trisicell can be used for scalable tumor phylogeny reconstruction and validation through both single-cell and clonal-subline sequencing data, which may reveal strong phenotypic associations. In particular, they suggest that the developmental status and phenotypic intratumoral heterogeneity of melanoma originates from observable subclonal variation. Citation Format: Farid Rashidi Mehrabadi, Salem Malikic, Kerrie L. Marie, Eva Perez-Guijarro, Erfan Sadeqi Azer, Howard H. Yang, Can Kizilkale, Charli Gruen, Huaitian Liu, Christina Marcelus, Aydin Buluc, Funda Ergun, Maxwell P. Lee, Glenn Merlino, Chi-Ping Day, S. Cenk Sahinalp. Trisicell: Scalable Tumor Phylogeny Reconstruction and Validation Reveals Developmental Origin and Therapeutic Impact of Intratumoral Heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB019.