Dataset Information

Ploidy-Seq: inferring mutational chronology by sequencing polyploid tumor subpopulations.

ABSTRACT: Human cancers are frequently polyploid, containing multiple aneuploid subpopulations that differ in total DNA content. In this study we exploit this property to reconstruct evolutionary histories, by assuming that mutational complexity increases with time. We developed an experimental method called Ploidy-Seq that uses flow-sorting to isolate and enrich subpopulations with different ploidy prior to next-generation genome sequencing. We applied Ploidy-Seq to a patient with a triple-negative (ER-/PR-/HER2-) ductal carcinoma and performed whole-genome sequencing to trace the evolution of point mutations, indels, copy number aberrations, and structural variants in three clonal subpopulations during tumor growth. Our data show that few mutations (8% to 22%) were shared between all three subpopulations, and that the most aggressive clones comprised a minority of the tumor mass. We expect that Ploidy-Seq will have broad applications for delineating clonal diversity and investigating genome evolution in many human cancers.

SUBMITTER: Malhotra A

PROVIDER: S-EPMC4343275 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Ploidy-Seq: inferring mutational chronology by sequencing polyploid tumor subpopulations.

Malhotra Ankit A Wang Yong Y Waters Jill J Chen Ken K Meric-Bernstam Funda F Hall Ira M IM Navin Nicholas E NE

Genome medicine 20150128 1

Human cancers are frequently polyploid, containing multiple aneuploid subpopulations that differ in total DNA content. In this study we exploit this property to reconstruct evolutionary histories, by assuming that mutational complexity increases with time. We developed an experimental method called Ploidy-Seq that uses flow-sorting to isolate and enrich subpopulations with different ploidy prior to next-generation genome sequencing. We applied Ploidy-Seq to a patient with a triple-negative (ER-/ ...[more]

PMID: 25729435

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Project description:BackgroundTraditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor.ResultsIn this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (> 95%) are likely germline variants, leading to C > T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ.ConclusionsTaken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Dataset Information

Ploidy-Seq: inferring mutational chronology by sequencing polyploid tumor subpopulations.

Publications

Ploidy-Seq: inferring mutational chronology by sequencing polyploid tumor subpopulations.

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