Evidence map›Paper›PMID 30991985›Full record

ArticleBMC cancer2019

Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients.

Krithika Bhuvaneshwar, Michael Harris, Yuriy Gusev, Subha Madhavan, Ramaswamy Iyer, Thierry Vilboux, John Deeken, Elizabeth Yang, Sadhna Shankar

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
1.1field-weighted citation impact, top 28% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

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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

9 authors at 4 institutions in 1 country.

Krithika BhuvaneshwarInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA. kb472@georgetown.edu.ORCID http://orcid.org/0000-0003-4015-7056
Michael HarrisInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA.
Yuriy GusevInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA.
Subha MadhavanInnovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA.
Ramaswamy IyerInova Translational Medicine Institute, Fairfax, VA, USA.
Thierry VilbouxInova Translational Medicine Institute, Fairfax, VA, USA.
John DeekenInova Translational Medicine Institute, Fairfax, VA, USA.
Elizabeth YangInova Children's Hospital, Falls Church, VA, USA.
Sadhna ShankarInova Children's Hospital, Falls Church, VA, USA.
Georgetown University · USInova Fairfax Hospital · USCenter for Cancer and Blood Disorders · USVirginia Commonwealth University · US

Funding

Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI Geoffrey Gibney · 1990 to 2026
$71.5M
NCI NIH HHS P30 CA051008
6 · The paper itself

Abstract

backgroundOsteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients.

methodsWe used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival.

resultsWe found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance.

conclusionWe show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.

Indexed as

GenomicsGerm-Line MutationAllelesBiomarkers, TumorBlood CellsBone NeoplasmsDrug Resistance, NeoplasmGene FrequencyGenotypeHigh-Throughput Nucleotide SequencingHumansKaplan-Meier EstimateOsteosarcomaPolymorphism, Single NucleotidePrognosisBiomarkers, TumorChildhood cancersDrug resistanceGeneticsOsteosarcomaPharmacogenomicsWhole genome sequencing

Identifiers

PMID30991985
PMCPMC6466653
OpenAlexW2944483474

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.