Evidence map›Paper›PMID 33771218›Full record

ArticleGenome medicine2021

OncoGEMINI: software for investigating tumor variants from multiple biopsies with integrated cancer annotations.

Thomas J Nicholas, Michael J Cormier, Xiaomeng Huang, Yi Qiao, Gabor T Marth, Aaron R Quinlan

Abstract read
In one paragraph

Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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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

6 authors.

Thomas J NicholasDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
Michael J CormierDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
Xiaomeng HuangDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
Yi QiaoDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
Gabor T MarthDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
Aaron R QuinlanDepartment of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA. aaronquinlan@gmail.com.ORCID 0000-0003-1756-0859

Funding

Monitoring tumor subclonal heterogeneity over time and spaceU24CA209999 · NCI · UNIVERSITY OF UTAH · PI MARTH, GABOR T, QUINLAN, AARON R · 2016 to 2020
$3.9M
Software for exploring all forms of genetic variation in any speciesR01GM124355 · NIGMS · UNIVERSITY OF UTAH · PI QUINLAN, AARON R · 2017 to 2020
$1.8M
NRSA Training CoreTL1TR002540 · NCATS · UNIVERSITY OF UTAH · PI FAGERLIN, ANGELA, FAIRFAX, KEKE CELESTE · 2018 to 2022
$1.6M
NCATS NIH HHS TL1 TR002540NCI NIH HHS U24 CA209999NIGMS NIH HHS R01 GM124355
6 · The paper itself

Abstract

backgroundDNA sequencing has unveiled extensive tumor heterogeneity in several different cancer types, with many exhibiting diverse subclonal populations. Identifying and tracing mutations throughout the expansion and progression of a tumor represents a significant challenge. Furthermore, prioritizing the subset of such mutations most likely to contribute to tumor evolution or that could serve as potential therapeutic targets represents an ongoing problem.

resultsHere, we describe OncoGEMINI, a new tool designed for exploring the complex patterns and trajectory of somatic and inherited variation observed in heterogeneous tumors biopsied over the course of treatment. This is accomplished by creating a searchable database of variants that includes tumor sampling time points and allows for filtering methods that reflect specific changes in variant allele frequencies over time. Additionally, by incorporating existing annotations and resources that facilitate the interpretation of cancer mutations (e.g., CIViC, DGIdb), OncoGEMINI enables rapid searches for, and potential identification of, mutations that may be driving subclonal evolution.

conclusionsBy combining relevant genomic annotations alongside specific filtering tools, OncoGEMINI provides powerful and customizable approaches that enable the quick identification of individual tumor variants that meet specified criteria. It can be applied to a wide range of tumor-derived sequence data, but is especially designed for studies with multiple samples, including longitudinal datasets. It is available under an MIT license at github.com/fakedrtom/oncogemini .

Indexed as

Genetic VariationSoftwareBiopsyBreast NeoplasmsDatabases, GeneticFemaleHumansLongitudinal StudiesNeoplasm Metastasis

Identifiers

PMID33771218
PMCPMC7995589

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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.