Quantification of subclonal selection in cancer from bulk sequencing data

Williams, Marc; Werner, Benjamin; Heide, Timon; Curtis, Christina; Barnes, C.; Sottoriva, Andrea; Graham, Trevor A.

doi:10.1038/s41588-018-0128-6

Cited by 242 publications

(261 citation statements)

References 58 publications

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“…For instance, in P6, we identified 3 prominent mutation clusters; all 3 were present in all regions in the tumor, but at varying proportions ( Figure 1D). In addition, we adopted an orthogonal approach, and applied the method proposed by Williams et al (Williams et al, 2018) to assess clonal architecture and compare that with our estimates. Overall, the clonal architecture the tumors are similar to that inferred by PyClone, with 1-2 subclones per sample (Supplementary Figure 10).…”

Section: Patterns Of Genetic Heterogeneitymentioning

confidence: 99%

“…We also used a Bayesian approach developed by Williams et al (Williams et al, 2018), as implemented in their SubClonalSelection.jl to draw clonal inferences from variant allele frequencies. Subclonal architecture of each sample was detected and the selective advantage (s) and time of appearance of each subclone (t) were measured simultaneously, as recommended by the authors.…”

Section: Identification Of Subclones and Evolutionary Dynamics Paramementioning

confidence: 99%

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Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

Sharma

Merritt

Cruz

et al. 2019

Preprint

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Impacts of genetic and non-genetic intra-tumor heterogeneity (ITH) on tumor phenotypes and evolvability remain debated. We analyzed ITH in lung squamous cell carcinoma (LUSC) at the levels of genome, transcriptome, tumor-immune interactions, and histopathological characteristics by multi-region profiling and using single-cell sequencing data. Overall, in LUSC genomic heterogeneity alone was a weak indicator of intra-tumor non-genetic heterogeneity at immune and transcriptomic levels that impacted multiple cancer-related pathways including those related to proliferation and inflammation, which in turn contributed to intra-tumor regional differences in histopathology and subtype classification. Genome, transcriptome, and immune-level heterogeneity influenced different aspects of tumor evolution. Tumor subclones had substantial differences in proliferation score, suggestive of non-neutral clonal dynamics. Scores for proliferation and other cancer-related pathways also showed intra-tumor regional differences, sometimes even within the same subclones. Neo-epitope burden negatively correlated with immune infiltration, indicating potential immune-mediated purifying selection on acquired mutations in these tumors. Taken together, our observations suggest that non-genetic heterogeneity is a major determinant of heterogeneity in histopathological characteristics and impacts evolutionary dynamics in lung cancer.

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Section: Patterns Of Genetic Heterogeneitymentioning

confidence: 99%

Section: Identification Of Subclones and Evolutionary Dynamics Paramementioning

confidence: 99%

Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

Sharma

Merritt

Cruz

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…S1). As several advanced studies have shown, [7][8][9]13,16,17 the proposed method also shows the advantage of using VAF information (mutation read counts and depths) rather than using only the presence or absence of mutations, to infer the tumor evolutionary process.…”

Section: Discussionmentioning

confidence: 99%

“…Second, we considered the case that one positively selective subclone in the primary tumor appears in the WES samples. 13 One starting primary tumor cell with b = 1, d = 0.1 are assumed to evolve and at the time when the population reaches a particular population size, N sub. occ.…”

Section: Robustness For Cell Death and Selection In The Primary Tumormentioning

confidence: 99%

Quantification of multicellular colonization in tumor metastasis using exome‐sequencing data

Nishino

Watanabe

Miya

et al. 2020

Intl Journal of Cancer

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Metastasis is a major cause of cancer‐related mortality, and it is essential to understand how metastasis occurs in order to overcome it. One relevant question is the origin of a metastatic tumor cell population. Although the hypothesis of a single‐cell origin for metastasis from a primary tumor has long been prevalent, several recent studies using mouse models have supported a multicellular origin of metastasis. Human bulk whole‐exome sequencing (WES) studies also have demonstrated a multiple “clonal” origin of metastasis, with different mutational compositions. Specifically, there has not yet been strong research to determine how many founder cells colonize a metastatic tumor. To address this question, under the metastatic model of “single bottleneck followed by rapid growth,” we developed a method to quantify the “founder cell population size” in a metastasis using paired WES data from primary and metachronous metastatic tumors. Simulation studies demonstrated the proposed method gives unbiased results with sufficient accuracy in the range of realistic settings. Applying the proposed method to real WES data from four colorectal cancer patients, all samples supported a multicellular origin of metastasis and the founder size was quantified, ranging from 3 to 17 cells. Such a wide‐range of founder sizes estimated by the proposed method suggests that there are large variations in genetic similarity between primary and metastatic tumors in the same subjects, which may explain the observed (dis)similarity of drug responses between tumors.

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“…Fitness landscape-based methods could be used to measure how cancer cells adapt to treatments in vitro and there has been some success in measuring patient specific subclonal selection coefficients 43 .…”

Section: Discussionmentioning

confidence: 99%

Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy

Jiang

Tomlinson

2018

Preprint

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AbstractOne of the great unsolved puzzles in cancer biology is not why cancers occur, but rather, explaining why so few cancers occur compared with the theoretical number that could occur given the number of progenitor cells in the body and the normal mutation rate. We hypothesised that a contributory explanation is that the tumour microenvironment (TME) is not fixed, and that this could impair the ability of neoplastic cells to retain a high enough fitness to become a cancer. The TME has implicitly been assumed to be static in most cancer evolution models, and we therefore developed a mathematical model of spatial cancer evolution assuming that the TME, and thus the optimum cancer phenotype, change over time. Based on simulations, we show how cancer cell populations adapt to diverse changing TME conditions and fitness landscapes. Compared with static TMEs which generate neutral dynamics, changing TMEs lead to complex adaptations with spatio-temporal heterogeneity involving variable sub-clonal fitness, mixing, competition and phylogeny patterns. In many cases, cancer cell populations fail to grow or undergo spontaneous regression, and even extinction. Our analyses predict that cancer evolution in a changing TME is challenging, and can help to explain why cancer is neither inevitable nor as common as expected. Should cancer driver mutations with effects dependent of the TME exist, they are likely to be selected. Anti-cancer prevention and treatment strategies based on changing the TME are feasible and potentially effective.

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Quantification of subclonal selection in cancer from bulk sequencing data

Cited by 242 publications

References 58 publications

Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors

Quantification of multicellular colonization in tumor metastasis using exome‐sequencing data

Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy

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