Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

Salehi, Sohrab; Dorri, Fatemeh; Chern, Kevin; Kabeer, Farhia; Rusk, Nicole; Funnell, Tyler; Williams, Marc; Lai, Daniel; Andronescu, Mirela; Campbell, Kieran R; McPherson, Andrew; Aparicio, Samuel; Roth, Andrew; Shah, Sohrab P.; Bouchard‐Côté, Alexandre

doi:10.24072/pcjournal.292

Cited by 15 publications

(14 citation statements)

References 64 publications

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“…To assign scRNA-seq transcriptomes to clones, we first generated CNA phylogenetic trees with a Bayesian [19] method from DLP+ single cell genome sequencing of 6 patient PDX lines, to determine the genome fraction occupied by TNBC breast cancer clones. For each patient series we identified CNA clonal populations from major clades (Figure 2a), and copy number profiles for each clone (Figure 2b,c, Supplementary Figures 2-5).…”

Section: Resultsmentioning

confidence: 99%

“…Firstly, the read data from DLP+ single cell sequencing was preprocessed using HMMcopy package version 1.32.0 to provide a copy number profile for each cell with 500KB genomic bin regions [43]. Secondly, we reconstructed phylogenetic trees by applying sitka [19], an efficient Bayesian tree inference method to copy number data. Cells are placed at the terminal leaf nodes in the phylogenetic tree, and cells with high similarity in copy number profiles are placed in the neighbourhood area of the same branch in the tree.…”

Section: Phylogenetic Tree Inference Clone Determination and Clonal A...mentioning

confidence: 99%

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Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

Kabeer,

Tran,

Andronescu

et al. 2023

Preprint

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Background: The encoding of cell intrinsic resistance states in breast cancer reflects the contributions of genomic and non-genomic variation. However, identifying the potential contributions of each requires accurate measurement and subtraction of the contribution of clonal fitness from co-measurement of transcriptional states. Somatic genomic variation in gene dosage, copy number variation, is the dominant mutational mechanism in breast cancer contributing to transcriptional variation and has recently been shown to contribute to platinum chemotherapy resistance states. Here we deploy time series measurements of triple negative breast cancer single cell transcriptomes in conjunction with co-measured single cell copy number associated clonal fitness to identify the contributions of genomic and non-genomic mechanisms to drug associated transcription states. Results: We generated serial scRNA-seq data (126,556 cells) from triple negative breast cancer (TNBC) patient-derived xenograft (PDX) experiments over 2.5 years in duration, and matched it against genomic copy number single cell data from the same biological samples. We show that the cell memory of transcriptional states of TNBC tumors serially exposed to platinum identifies distinct clonal responses within individual tumours. Copy-number clones with high drug fitness leading to clonal sweeps exhibit less transcriptional reversion, whereas clones with weak drug fitness exhibit highly dynamic transcription on drug withdrawal. Pathway analysis shows that copy number associated and copy number independent transcripts converge on epithelial-mesenchymal transition (EMT) and cytokine signaling states associated with resistance. We show from trajectory analysis that transcriptional reversion exhibits hysteresis, indicating that new intermediate transcriptional states are generated by platinum exposure. Conclusions: We discovered that copy number clones with strong genotype associated fitness under platinum became fixed in their states, resulting in minimal transcriptional reversion on drug withdrawal. In contrast clones with weaker fitness undergo non-genomic transcriptional plasticity and these distinct responses co-exist within single tumours. Together the data suggest that copy number associated and copy number independent transcriptional states may contribute to platinum drug resistance within individual tumours. The dominance of genomic or non-genomic mechanisms within individual polyclonal tumours has implications for approaches to restoration of drug sensitivity and re-treatment strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Phylogenetic Tree Inference Clone Determination and Clonal A...mentioning

confidence: 99%

Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

Kabeer,

Tran,

Andronescu

et al. 2023

Preprint

Self Cite

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“…Most S-phase cells should be present in the unknown initial set. High-confidence G1/2-phase cells are phylogenetically clustered into clones based on CN using methods such as sitka [58] or MEDICC2 [59]. Optionally, users can provide their own sets of clustered high-confidence G1/2-phase and unknown cells.…”

Section: Pert Modelmentioning

confidence: 99%

“…We used the clone IDs from Funnell et al for high-confidence G1/2 cells [40]. These single-cell phylogenetic trees were generated using sitka [58]. Sitka uses CN breakpoints (also referred to as changepoints) across the genome as binary input characters to construct the evolutionary relationships between cells.…”

Section: Phylogenetic Clustering Based On Cn Profilesmentioning

confidence: 99%

Single-cell DNA replication dynamics in genomically unstable cancers

Weiner

Williams

Shi

et al. 2023

Preprint

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DNA replication is a highly coordinated cell cycle process that can become dysregulated in cancer, increasing both proliferation and mutation rates. Single-cell whole genome sequencing holds potential for studying replication dynamics of cancer cells; however, computational methods for identifying S-phase cells and inferring single-cell replication timing profiles remain immature for samples with heterogeneous copy number. Here we report a new method, PERT, which jointly infers replication and somatic copy number states of S-phase cells. This method enabled us to analyze the replication dynamics of >10,000 S-phase single-cell genomes across various triple negative breast cancers and cell lines with subclonal copy number heterogeneity. We show that PERT robustly predicts cell cycle phase, quantifies replication timing variability, and approximates relative proliferation rates between tumor subclones. Our results illuminate how aberrant DNA replication processes can both drive and result from evolution of human tumors.

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“…The resulting tumor heterogeneity is a substantial barrier for successful treatment [10]. To analyze the genetic content of a tumor, the recent single cell whole genome sequencing (scWGS) technology Direct Library Preparation (DLP) [35], has shown proficiency in producing data of thousands of single cells suitable for copy number (CN) analysis [30, 35]. Yet, the data exhibits imperfections and the experimental process is inherently noisy.…”

Section: Introductionmentioning

confidence: 99%

VICTree - a Variational Inference method for Clonal Tree reconstruction

Melin,

Zampinetti,

McPherson

et al. 2024

Preprint

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Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a Variational Inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTree's capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTree's results are compared in terms of quality and speed of inference to other state-of-the art methods. The code for VICTree is available on GitHub: github.com/Lagergren-Lab/victree.

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Cancer phylogenetic tree inference at scale from 1000s of single cell genomes

Abstract: Bouchard-Côté (2023) Cancer phylogenetic tree inference at scale from 1000s of single cell genomes , Peer Community Journal, 3: e63.

Cited by 15 publications

References 64 publications

Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

Single cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

Single-cell DNA replication dynamics in genomically unstable cancers

VICTree - a Variational Inference method for Clonal Tree reconstruction

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