MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution

Understanding the evolutionary pathways to metastasis and resistance to immune checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here we present the most comprehensive intra-patient metastatic melanoma dataset assembled to date as part of the PEACE research autopsy programme, including 222 exome, 493 panel-sequenced, 161 RNA-seq, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI non-responders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one of the patients. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma.

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“…To assess the patterns of evolution at the level of copy number, we used MEDICC2 (27) to build SCNA sample trees (Supplementary Fig. S5).…”

Section: Figurementioning

confidence: 99%

Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways

et al. 2023

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“…Early methods for computing evolutionary distances on copy number data [3,7] employed simple measures of distance such as the Hamming, weighted Hamming, and ℓ 1 distance between copy number profiles. However, these distances do not account for dependencies between loci caused by long CNAs spanning contiguous segments of the genome, leading to inaccurate phylogenetic reconstruction [38,22].…”

Section: Copy Number Transformationsmentioning

confidence: 99%

“…In this section, we describe and investigate the copy number transformation (CNT) model, one of the most well-known and successful evolutionary models for copy number evolution in cancer. The CNT model was originally introduced in MEDICC [38] and extended in subsequent studies [51,11,50,8,22]. Since the CNT model only allows intrachromosomal copy number events, it is sufficient to consider the case of a single chromosome, and thus for ease of exposition we will describe the model using a single chromosome.…”

Section: Copy Number Transformationsmentioning

confidence: 99%

“…The CNT distance is computable in linear time [51] and has been used to define an evolutionary distance between profiles. Since the CNT distance is not symmetric, a variety of symmetrized CNT distances have also been used to construct copy number phylogenies using distance-based phylogenetic methods [38,11,50,22]. Further, owing to its effectiveness, the CNT model has become the basis of a variety of distinct models [50,22,8] for copy number evolution.…”

Section: Introductionmentioning

confidence: 99%

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A zero-agnostic model for copy number evolution in cancer

Schmidt

Sashittal

Raphael

2023

Preprint

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Motivation: New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results: We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.

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“…Although phylogeny inference methods from bulk sequencing and cell clustering from single-cell RNA sequencing are expanding to incorporate both SNV and CNA features, such as TUSV-ext [10] and CA-SIC [11], current methods for tumor phylogeny and/or clone inference from single-cell sequencing naturally tend to focus on the features (SNV or CNA events) for which the data is ideally suited [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. One exception in the medium to high coverage scDNA-seq regime is SCARLET [27], which refines a given copy number tree using SNV read counts under a CNA loss supported evolutionary model.…”

Section: Introductionmentioning

confidence: 99%

Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

Weber

Zhang

Ochoa

et al. 2022

Preprint

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MotivationEmerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to simultaneously analyze the evolution of SNVs alongside CNAs from the same set of tumor cells.ResultsWe present Phertilizer, a method to infer a clonal tree, capturing both SNV and CNA evolution, from ultra-low coverage single-cell DNA sequencing of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of Phertilizer on simulated data as well as on two real datasets, finding that Phertilizer effectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.Availabilityhttps://github.com/elkebir-group/phertilizerContact{idoia,melkebir}@illinois.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution

Cited by 59 publications

References 76 publications

Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways

Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways

A zero-agnostic model for copy number evolution in cancer

Phertilizer: Growing a Clonal Tree from Ultra-low Coverage Single-cell DNA Sequencing of Tumors

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