A population genetics perspective on the determinants of intra-tumor heterogeneity

Hu, Zheng; Sun, Ruping; Curtis, Christina

doi:10.1016/j.bbcan.2017.03.001

Cited by 44 publications

(42 citation statements)

References 285 publications

(448 reference statements)

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“…The ‘Big Bang’ model, which is based on the concept of punctuated evolution (13, 14), has provided a novel framework to understand the clonal evolution and the timing of the emergence of intratumoral heterogeneity (ITH) in CRC. This model posits that the vast majority of genomic alterations accumulate during early stages of carcinogenesis, while the tumor mass is still less than one million cells, and before progressing into an advanced neoplasm.…”

Section: Introductionmentioning

confidence: 99%

Mutational Heterogeneity in APC and KRAS Arises at the Crypt Level and Leads to Polyclonality in Early Colorectal Tumorigenesis

Gausachs

Borràs

Chang

et al. 2017

Clinical Cancer Research

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Purpose The majority of genomic alterations causing intratumoral heterogeneity (ITH) in colorectal cancer (CRC) are thought to arise during early stages of carcinogenesis as a burst but only after truncal mutations in APC have expanded a single founder clone. We have investigated if the initial source of ITH is consequent to multiple independent lineages derived from different crypts harboring distinct truncal APC and driver KRAS mutations, thus challenging the prevailing monoclonal monocryptal model. Experimental design High-depth next-generation sequencing and SNP arrays were performed in whole lesion extracts of 37 FAP colorectal adenomas. Also, ultra-sensitive genotyping of hotspot mutations of APC and KRAS was performed using nanofluidic PCRs in matched bulk biopsies (n=59) and crypts (n=591) from 18 adenomas and 7 carcinomas and adjacent normal tissues. Results Multiple co-occurring truncal APC and driver KRAS alterations were uncovered in whole lesion extracts from adenomas and subsequently confirmed to belong to multiple clones. Ultra-sensitive genotyping of bulk biopsies and crypts revealed novel undetected APC mutations that were prominent among carcinomas, whereas abundant wild-type APC crypts were detected in adenomas. KRAS mutational heterogeneity within crypts was evident in both adenomas and carcinomas with a higher degree of concordance between biopsy and crypt genotyping in carcinomas. Non-random heterogeneity among crypts was also observed. Conclusions The striking degree of non-random intercrypt heterogeneity in truncal and driver gene mutations observed in adenomas and carcinomas is consistent with a polycryptal model derived from multiple independent initiation linages as the source of early ITH in colorectal carcinogenesis.

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Section: Introductionmentioning

confidence: 99%

Mutational Heterogeneity in APC and KRAS Arises at the Crypt Level and Leads to Polyclonality in Early Colorectal Tumorigenesis

Gausachs

Borràs

Chang

et al. 2017

Clinical Cancer Research

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“…A large number of approaches have been published over the last years that try to identify subclones, their frequencies, and in some cases, their phylogenetic relationships by deconvolving these aggregate data [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. However, the underlying statistical problem is underdetermined [24,25], and mutations with similar VAFs are automatically clustered into a single subclone. This inevitably leads to incorrect phylogenies for tumours with multiple distinct subclones of similar prevalences.…”

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confidence: 99%

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Malikić

Jahn

Kuipers

et al. 2017

Preprint

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Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.

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“…LICHeE also uses the patterns of mutation presence and absence among tumor samples to generate SNV clusters and thus harnesses evolutionary information in the data. We expect many real world datasets to exhibit this evolutionary property in which similar clones are inherited by descendant tumors and distantly-related clones are found in samples from distantly located tumor or regions of a tumor, which means that tumor expansion is coupled with the evolution of new clones (Davis et al, 2017;Gerlinger et al, 2014;Gerlinger et al, 2012;Hu et al, 2017). Many other methods did not perform well for these datasets, because they seem to not use the intrinsic evolutionary information effectively.…”

Section: Discussionmentioning

confidence: 99%

Predicting clone genotypes from tumor bulk sequencing of multiple samples

Miura

Gómez

Murillo

et al. 2018

Preprint

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Motivation: Analyses of data generated from bulk sequencing of tumors have revealed extensive genomic heterogeneity within patients. Many computational methods have been developed to enable the inference of genotypes of tumor cell populations (clones) from bulk sequencing data. However, the relative and absolute accuracy of available computational methods in estimating clone counts and clone genotypes is not yet known. Results:We have assessed the performance of nine methods, including eight previously-published and one new method (CloneFinder), by analyzing computer simulated datasets. CloneFinder, LICHeE, CITUP, and cloneHD inferred clone genotypes with low error (<5% per clone) for a majority of datasets in which the tumor samples contained evolutionarily-related clones. Computational methods did not perform well for datasets in which tumor samples contained mixtures of clones from different clonal lineages. Generally, the number of clones was underestimated by cloneHD and overestimated by Phy-loWGS, and BayClone2, Canopy, and Clomial required prior information regarding the number of clones. AncesTree and Canopy did not produce results for a large number of datasets. Conclusions: Deconvolution of clone genotypes from single nucleotide variant (SNV) frequency differences among tumor samples remains challenging, so there is a need to develop more accurate computational methods and robust software for clone genotype inference.

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A population genetics perspective on the determinants of intra-tumor heterogeneity

Cited by 44 publications

References 285 publications

Mutational Heterogeneity in APC and KRAS Arises at the Crypt Level and Leads to Polyclonality in Early Colorectal Tumorigenesis

Mutational Heterogeneity in APC and KRAS Arises at the Crypt Level and Leads to Polyclonality in Early Colorectal Tumorigenesis

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Predicting clone genotypes from tumor bulk sequencing of multiple samples

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