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

Krithika BhuvaneshwarEmail author View ORCID ID profile,
Michael Harris,
Yuriy Gusev,
Subha Madhavan,
Ramaswamy Iyer,
Thierry Vilboux,
John Deeken,
Elizabeth Yang and
Sadhna Shankar

BMC Cancer201919:357

https://doi.org/10.1186/s12885-019-5474-y

Received: 10 November 2017
Accepted: 14 March 2019
Published: 16 April 2019

Open Peer Review reports

Abstract

Background

Osteosarcoma 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.

Methods

We 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.

Results

We 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.

Conclusion

We 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.