Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours
Author(s)
Naulaerts, Stefan
Dang, Cuong C
Ballester, Pedro J
Type
Journal Article
Abstract
Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.
Date Issued
2017-11-14
Date Acceptance
2017-08-14
Citation
Oncotarget, 2017, 8 (57), pp.97025-97040
ISSN
1949-2553
Publisher
Impact Journals
Start Page
97025
End Page
97040
Journal / Book Title
Oncotarget
Volume
8
Issue
57
Copyright Statement
Copyright: Naulaerts et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License
3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.
3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.
License URL
Identifier
https://doi.org/10.18632%2Foncotarget.20923
Subjects
Science & Technology
Life Sciences & Biomedicine
Oncology
Cell Biology
biomarker discovery
machine learning
drug sensitivity
genomics
cancer
BINDING-AFFINITY
MODEL SELECTION
CELL-LINES
CANCER
PREDICTION
DISCOVERY
THERAPY
DESIGN
PANEL
BIAS
biomarker discovery
cancer
drug sensitivity
genomics
machine learning
1112 Oncology and Carcinogenesis
Publication Status
Published
Article Number
57
Date Publish Online
2017-09-15