Predicting clone genotypes from tumor bulk sequencing of multiple samples

Miura, Sayaka; Gómez, Karen; Murillo, Óscar; Huuki, Louise A; Vu, Tracy; Buturla, Tiffany; Kumar, Sudhir

doi:10.1093/bioinformatics/bty469

Cited by 29 publications

(48 citation statements)

References 41 publications

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“…However, the vast accumulation of gene expression data generated from clinical tumor samples is primarily collected via bulk tumor RNA sequencing or microarray analysis. These datasets represent the transcriptional output of all stromal and malignant cells combined, making it difficult to deconvolute deviations accounted solely by cancer cell response [6]. As a result of this technical challenge, heterogeneity found in a pre-clinical screening has classically been underrepresented and initial screens are typically performed on in vitro cultures of solely cancer cells.…”

Section: Introductionmentioning

confidence: 99%

Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo

Hum

Sebastian

Gilmore

et al. 2020

Cancers

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Current pre-clinical models of cancer fail to recapitulate the cancer cell behavior in primary tumors primarily because of the lack of a deeper understanding of the effects that the microenvironment has on cancer cell phenotype. Transcriptomic profiling of 4T1 murine mammary carcinoma cells from 2D and 3D cultures, subcutaneous or orthotopic allografts (from immunocompetent or immunodeficient mice), as well as ex vivo tumoroids, revealed differences in molecular signatures including altered expression of genes involved in cell cycle progression, cell signaling and extracellular matrix remodeling. The 3D culture platforms had more in vivo-like transcriptional profiles than 2D cultures. In vivo tumors had more cells undergoing epithelial-to-mesenchymal transition (EMT) while in vitro cultures had cells residing primarily in an epithelial or mesenchymal state. Ex vivo tumoroids incorporated aspects of in vivo and in vitro culturing, retaining higher abundance of cells undergoing EMT while shifting cancer cell fate towards a more mesenchymal state. Cellular heterogeneity surveyed by scRNA-seq revealed that ex vivo tumoroids, while rapidly expanding cancer and fibroblast populations, lose a significant proportion of immune components. This study emphasizes the need to improve in vitro culture systems and preserve syngeneic-like tumor composition by maintaining similar EMT heterogeneity as well as inclusion of stromal subpopulations.

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Section: Introductionmentioning

confidence: 99%

Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo

Hum

Sebastian

Gilmore

et al. 2020

Cancers

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“…Since the accuracy of the clonal deconvolution from mixed samples largely depends on the quality of the inferred VAFs, and copy-number variation is known to alter the allele frequency of somatic mutations in bulk tumor samples, somatic calls showing a VAF < 0.075, with a read depth < 20 in all tumor and healthy samples, and/or overlapping with copy-number events were filtered out prior to clonal deconvolution. The number of tumor clones, as well as their genotype sequences, were then inferred using the CloneFinder algorithm 18 , which has been previously shown to outperform other methods in both simulated and empirical datasets (but see Supplementary Information).…”

Section: Methodsmentioning

confidence: 99%

“…In particular, it presumes that the clonal genotypes were appropriately reconstructed. Indeed, clonal deconvolution remains a very hard problem 18 , and we cannot rule out some degree of uncertainty in the precise combination of mutations assigned to any given clone. Nevertheless, we were reassured to some extent by the fact that comparable clonal genotypes were obtained when using a different deconvolution approach 19 (Supplementary Fig.…”

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confidence: 99%

Rapid evolution and biogeographic spread in a colorectal cancer

Alves

Prado‐Lopez

Cameselle-Teijeiro

et al. 2019

Preprint

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How and when tumoral clones start spreading to surrounding and distant tissues is currently 1 unclear. Here, we leveraged a model-based evolutionary framework to investigate the 2 demographic and biogeographic history of a colorectal cancer. Our analyses strongly support 3 an early monoclonal metastatic colonization, followed by a rapid population expansion at both 4 primary and secondary sites. Moreover, we infer a hematogenous metastatic spread seemingly 5 under positive selection, plus the return of some tumoral cells from the liver back to the colon 6 lymph nodes. This study illustrates how sophisticated techniques typical of organismal 7 evolution can provide a detailed picture of the complex tumoral dynamics over time and space. 8

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“…This is done by comparing the observed variance of VAFs with the expected variance of VAFs. Specifically, given a clade containing variants, we assume they belong to the same subclone and compute and by solving equation (1). We then draw random samples 1:~( , ) and calculate ( ) .…”

Section: Magos Algorithmmentioning

confidence: 99%

“…The development of a tumor is an evolutionary process that typically initiates from a single clone and grows into a diverse population of cells via incessant mutations and selections (1)(2)(3). As a tumor progresses over time and space, different cell populations (i.e., subclones) emerge, expand and diminish, leading to a heterogeneous malignancy with multifarious clinical presentations.…”

Section: Introductionmentioning

confidence: 99%

MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths

Ahmadinejad

Troftgruben

Maley

et al. 2019

Preprint

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Understanding intratumor heterogeneity is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deepsequencing data (>300x depth). To enable accurate subclonal discovery in tumors sequenced at standard depths (30-50x), we develop a novel computational method that incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the meanvariance dependency of sequencing data at the subclonal level. Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. Applications of MAGOS to whole-exome sequencing data of 331 liver cancer samples discovered a significant association between subclonal diversity and patient overall survival.MAGOS is freely available as an R package at github (https://github.com/liliulab/magos).

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Predicting clone genotypes from tumor bulk sequencing of multiple samples

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 29 publications

References 41 publications

Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo

Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo

Rapid evolution and biogeographic spread in a colorectal cancer

MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths

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