Inferring parsimonious migration histories for metastatic cancers

El-Kebir, Mohammed; Satas, Gryte; Raphael, Benjamin J.

doi:10.1038/s41588-018-0106-z

Cited by 97 publications

(176 citation statements)

References 78 publications

(115 reference statements)

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“…A method that distinguishes both effects will be future work. In addition, there are possibly more complex cell migration patterns than our model, including reseeding or multisource seeding 16,17 , which are beyond the present study but worth investigating.…”

Section: Discussionmentioning

confidence: 98%

Quantification of multicellular colonization in tumor metastasis using exome sequencing data

Nishino

Watanabe

Miya

et al. 2019

Preprint

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Abbreviations: WES: whole-exome sequencing; CRC: colorectal cancer; VAF: variant allele frequency; IQR: interquartile range Novelty and impact: Applying our proposed method to the exome sequence data from four colorectal cancer patients, we showed the ʻmulti-cellular originʼ, not the classical ʻsingle-cell originʼ, of metastasis is correct, with founder sizes quantified in the range of 3 to 15 cells.These wide-ranging founder sizes suggest large variation in genetic similarity between both tumors, which may affect the (dis)similarity of drug response in primary and metastatic tumors. AbstractMetastasis 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 multi-cellular origin of metastasis. Human bulk whole-exome sequencing (WES) studies also have demonstrated a multiple ʻclonalʼ 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, we developed a method to quantify the ʻfounder cell population sizeʼ 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 multi-cellular origin of metastasis and the founder size was quantified, ranging from 3 to 15 cells. Such a wide-ranging 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 might be involved in (dis)similarity of drug responses between tumors.

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Section: Discussionmentioning

confidence: 98%

Quantification of multicellular colonization in tumor metastasis using exome sequencing data

Nishino

Watanabe

Miya

et al. 2019

Preprint

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“…This may show that there is room for improvement on the present simple model, for example, we should incorporate the stochastic metastatic evolution into the present model. Furthermore, there are possibly more complex cell migration patterns, including reseeding or multisource seeding, which are also beyond the present study, but worth investigating.…”

Section: Discussionmentioning

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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“…We leave exploring the trade-off between the two criteria as future work. We note that the transmission number criterion was introduced previously by Slatkin and Maddision [19], while a time-invariant version of the cotransmission number has been applied to the analyses migration in metastatic cancers [3,20]. Supplementary Table S2 provides nomenclature for topological features of transmission networks.…”

Section: Problem Statementmentioning

confidence: 99%

“…Given genomic and epidemiological data, the key challenge is to infer the evolutionary history of the pathogen isolates and the transmission history of the hosts. Importantly, while the phylogeny of the pathogen isolates captures the evolutionary history of the outbreak, it does not necessarily match the transmission history of the outbreak [2]-this mutationmigration discordance also arises in metastatic can-cers [3]. In particular, methods that assume that transmission events coincide with branching events in the phylogeny are only applicable in the context of pathogens with low mutation rates, short incubation times and acute infections [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

Sashittal

El-Kebir

2019

Preprint

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Background: Technological advances in genomic sequencing are facilitating the reconstruction of transmission histories during outbreaks in the fight against infectious diseases. However, accurate disease transmission inference using this data is hindered by a number of challenges due to within-host pathogen diversity and weak transmission bottlenecks, where multiple genetically-distinct pathogenic strains co-transmit. Results: We formulate a combinatorial optimization problem for transmission network inference under a weak bottleneck from a given timed phylogeny and establish hardness results. We present SharpTNI, a method to approximately count and almost uniformly sample from the solution space. Using simulated data, we show that SharpTNI accurately quantifies and uniformly samples from the solution space of parsimonious transmission networks, scaling to large datasets. We demonstrate that SharpTNI identifies co-transmissions during the 2014 Ebola outbreak that are corroborated by epidemiological information collected by previous studies. Conclusions: Accounting for weak transmission bottlenecks is crucial for accurate inference of transmission histories during outbreaks. SharpTNI is a parsimony-based method to reconstruct transmission networks for diseases with long incubation times and large inocula given timed phylogenies. The model and theoretical work of this paper pave the way for novel maximum likelihood methods to co-estimate timed phylogenies and transmission networks under a weak bottleneck.

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Inferring parsimonious migration histories for metastatic cancers

Cited by 97 publications

References 78 publications

Quantification of multicellular colonization in tumor metastasis using exome sequencing data

Quantification of multicellular colonization in tumor metastasis using exome sequencing data

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

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

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