scholarly journals Inferring structural variant cancer cell fraction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Marek Cmero ◽  
◽  
Ke Yuan ◽  
Cheng Soon Ong ◽  
Jan Schröder ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Cell Fraction ◽  
Structural Variant ◽  
Cancer Cell Fraction

scholarly journals SVclone: inferring structural variant cancer cell fraction

2017 ◽  
Author(s):  
Marek Cmero ◽  
Cheng Soon Ong ◽  
Ke Yuan ◽  
Jan Schröder ◽  
Kangbo Mo ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Copy Number ◽  
Computational Method ◽  
Cell Fraction ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Breast And Ovarian Cancer ◽  
Structural Variant ◽  
Cancer Cell Fraction

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant breakpoints from whole-genome sequencing data. We validate our approach using simulated and real tumour samples, and demonstrate its utility on 2,778 whole-genome sequenced tumours. We find a subset of liver, breast and ovarian cancer cases with decreased overall survival that have subclonally enriched copy-number neutral rearrangements, an observation that could not be discovered with currently available methods.

DNA methylation marker to estimate the breast cancer cell fraction in DNA samples

2018 ◽  
Vol 35 (11) ◽  
Author(s):  
Hiroki Ishihara ◽  
Satoshi Yamashita ◽  
Satoshi Fujii ◽  
Kazunari Tanabe ◽  
Hirofumi Mukai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cell Fraction ◽  
Methylation Marker ◽  
Cancer Cell Fraction

scholarly journals Establishment of a DNA methylation marker to evaluate cancer cell fraction in gastric cancer

2015 ◽  
Vol 19 (2) ◽  
pp. 361-369 ◽  
Author(s):  
Liang Zong ◽  
Naoko Hattori ◽  
Yukie Yoda ◽  
Satoshi Yamashita ◽  
Hideyuki Takeshima ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Cancer Cell ◽  
Cell Fraction ◽  
Methylation Marker ◽  
Cancer Cell Fraction

Genomic alterations driving breast cancer (BC) metastases and their relationship with the subtype switch in the GEICAM ConvertHER study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1017-1017 ◽  
Author(s):  
Joan Albanell ◽  
Abel Gonzalez ◽  
Ana M. Gonzalez-Angulo ◽  
Agda Karina Eterovic ◽  
Eduardo Martinez-De Duenas ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Intrinsic Subtype ◽  
Cell Fraction ◽  
Driver Mutations ◽  
Her2 Status ◽  
Genomic Alterations ◽  
Expression Arrays ◽  
Cancer Cell Fraction ◽  
Cell Fractions ◽  
Clinical Subtype

1017 Background: To understand the mechanisms underlying the evolution of tumors in the process of metastasis, we studied 61 paired primary-relapse BC from the GEICAM ConvertHER study. While some of the metastases maintained the clinical (ER/PR and HER2 status) and/or intrinsic subtype (defined by expression arrays) of the original tumor (concordant), others exhibited a subtype shift (discordant). We aimed to identify the genomic alterations driving the metastases and, particularly, their relationship with the subtype switch. Methods: We detected the somatic variants (mutations and copy number alterations (CNAs)) affecting 202 genes across the 61 sample pairs via targeted sequencing. We employed the Cancer Genome Interpreter (cancergenomeinterpreter.org), a bioinformatics approach to identify the alterations most likely driving tumorigenesis, and subsequently identified those whose cancer cell fraction markedly changed in the metastases. We explored the clonal remodeling in metastasis comparing the cell fractions of driver mutations in both concordant and discordant tumors. Results: We found that 156 genes had 747 somatic mutations and 171 genes suffered 1042 somatic CNAs in the 61 studied tumor pairs. We identified a median of 11 and 9 mutations in primaries and metastases, respectively. Several frequent BC mutational drivers, such as TP53, PIK3CA, MLL3, MAP3K1, and NOTCH2 were amongst the more frequently changed their cancer cell fraction in metastases with respect to primaries. We found that driver mutations of discordant tumors exhibited a significantly higher increase of clonal cell fraction. Moreover, whether the clonal status of a driver mutation was conserved in the metastasis was significantly associated to whether the tumor maintains its clinical subtype but not its intrinsic subtype. Conclusions: Our results suggest that a shift in the clinical subtype of BC undergoing metastasis is accompanied by more significant changes at the genomic level than those suffered by tumors that maintain their clinical subtype. This remodeling of the landscape of drivers could open new therapeutic opportunities to specifically target discordant BC.

Pancreatic Cancer Cell Fraction Estimation in a DNA Sample

Oncology ◽  
2018 ◽  
Vol 95 (6) ◽  
pp. 370-379 ◽  
Author(s):  
Hiroki Ishihara ◽  
Satoshi Yamashita ◽  
Ryosuke Amano ◽  
Kenjiro Kimura ◽  
Kosei Hirakawa ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Cell ◽  
Pancreatic Cancer Cell ◽  
Cell Fraction ◽  
Cancer Cell Fraction

scholarly journals Systematic Assessment of Tumor Purity and Its Clinical Implications

2020 ◽  
pp. 995-1005
Author(s):  
Syed Haider ◽  
Svitlana Tyekucheva ◽  
Davide Prandi ◽  
Natalie S. Fox ◽  
Jaeil Ahn ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Estimation Method ◽  
Cell Types ◽  
Cell Fraction ◽  
Estimation Methods ◽  
Tumor Purity ◽  
Genomic Analyses ◽  
Cancer Types ◽  
Molecular Profiles ◽  
Cancer Cell Fraction

PURPOSE The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor. MATERIALS AND METHODS To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell–specific mRNA and microRNA profiles. RESULTS We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types. CONCLUSION The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

scholarly journals DeCiFering the Elusive Cancer Cell Fraction in Tumor Heterogeneity and Evolution

2021 ◽  
Author(s):  
Gryte Satas ◽  
Simone Zaccaria ◽  
Mohammed El-Kebir ◽  
Benjamin J. Raphael
Keyword(s):  
Phylogenetic Analysis ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Cell Fraction ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Cancer Cell Fraction

AbstractMost tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) – e.g. most solid tumors – the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs.In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution.Code availabilitySoftware is available at https://github.com/raphael-group/decifer

scholarly journals DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

Cell Systems ◽  
2021 ◽  
Author(s):  
Gryte Satas ◽  
Simone Zaccaria ◽  
Mohammed El-Kebir ◽  
Benjamin J. Raphael
Keyword(s):  
Cancer Cell ◽  
Tumor Heterogeneity ◽  
Cell Fraction ◽  
Cancer Cell Fraction

scholarly journals Hypoxia promotes stem-like properties of laryngeal cancer cell lines by increasing the CD133+ stem cell fraction

2014 ◽  
Vol 44 (5) ◽  
pp. 1652-1660 ◽  
Author(s):  
CHUN-PING WU ◽  
HUAI-DONG DU ◽  
HONG-LI GONG ◽  
DA-WEI LI ◽  
LEI TAO ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Lines ◽  
Cancer Cell ◽  
Laryngeal Cancer ◽  
Cancer Cell Lines ◽  
Cell Fraction

scholarly journals Ccube: A fast and robust method for estimating cancer cell fractions

2018 ◽  
Author(s):  
Ke Yuan ◽  
Geoff Macintyre ◽  
Wei Liu ◽  
Florian Markowetz ◽  
Keyword(s):  
Cancer Cell ◽  
Model Fitting ◽  
Point Mutations ◽  
Whole Genome Sequencing Data ◽  
Probabilistic Framework ◽  
Sequencing Data ◽  
Inference Method ◽  
Cancer Cell Fraction ◽  
Cell Fractions ◽  
Quantifying Uncertainty

AbstractEstimating and clustering cancer cell fractions of genomic alterations are central tasks for studying intratumour heterogeneity. We present Ccube, a probabilistic framework for inferring the cancer cell fraction of somatic point mutations and the subclonal composition from whole-genome sequencing data. We develop a variational inference method for model fitting, which allows us to handle samples with large number of the variants (more than 2 million) while quantifying uncertainty in a Bayesian fashion. Ccube is available at https://github.com/keyuan/ccube.

Sign in / Sign up

Export Citation Format

Share Document