Evidence map›Paper›PMID 30239581›Full record

ArticleBriefings in bioinformatics2019

Revealing tumor heterogeneity of breast cancer by utilizing the linkage between somatic and germline mutations.

Meng Zou, Rui Jin, Kin Fai Au

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Meng ZouSchool of Mathematics and Statistics, Huazhong University of Science and Technology.
Rui JinDepartment of Statistics, University of Iowa.
Kin Fai AuSchool of Mathematics and Statistics, Huazhong University of Science and Technology.

Funding

Bioinformatics platform for Hybrid-Seq transcriptome data analysisR01HG008759 · NHGRI · UNIVERSITY OF IOWA · PI AU, KIN FAI · 2016 to 2020
$1.9M
NHGRI NIH HHS R01 HG008759
6 · The paper itself

Abstract

The intra-tumor heterogeneity is associated with cancer progression and therapeutic resistance, such as in breast cancer. While the existing methods for studying tumor heterogeneity only analyze variant allele frequency (VAF), the genotype of variant is also informative for inferring subclones, which can be detected by long reads or paired-end reads. We developed GenoClone to integrate VAF with the genotype of variant innovatively, so it showed superior performance of inferring the number of subclones, estimating the fractions of subclones and identifying somatic single-nucleotide variants composition of subclones. When GenoClone was applied to 389 TCGA breast cancer samples, it revealed extensive intra-tumor heterogeneity. We further found that a few somatic mutations were relevant to the late stage of tumor evolution, including the ones at the oncogene PIK3CA and the tumor suppress gene TP53. Moreover, 52 subclones that were identified from 167 samples shared high similarity of somatic mutations, which were clustered into three groups with the sizes of 24, 14 and 14. It is helpful for understanding the development of breast cancer in certain subgroups of people and the drug development for population level. Furthermore, GenoClone also identified the tumor heterogeneity in different aliquots of the same samples. The implementation of GenoClone is available at http://www.healthcare.uiowa.edu/labs/au/GenoClone/.

Indexed as

Genetic LinkageGerm-Line MutationBreast NeoplasmsClass I Phosphatidylinositol 3-KinasesFemaleGenotypeHumansMonte Carlo MethodPolymorphism, Single NucleotideTumor Suppressor Protein p53Class I Phosphatidylinositol 3-KinasesPIK3CA protein, humanTP53 protein, humanTumor Suppressor Protein p53germline mutationsomatic mutationsubclone inferencetumor heterogeneityVAF

Identifiers

PMID30239581
PMCPMC6954402

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.