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Comparative Study
. 2018 Nov 3;19(1):400.
doi: 10.1186/s12859-018-2430-9.

A comparative study of survival models for breast cancer prognostication revisited: the benefits of multi-gene models

Michal R Grzadkowski  1 , Dorota H Sendorek  1 , Christine P'ng  1 , Vincent Huang  1 , Paul C Boutros  2   3   4
Affiliations
Comparative Study

A comparative study of survival models for breast cancer prognostication revisited: the benefits of multi-gene models

Michal R Grzadkowski et al. BMC Bioinformatics. .

Abstract

Background: The development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this "single-gene hypothesis" using new techniques and datasets.

Results: By deeply sampling the population of available gene sets, we compare the intrinsic properties of single-gene biomarkers to multi-gene biomarkers in twelve different partitions of a large breast cancer meta-dataset. We show that simple multi-gene models consistently outperformed single-gene biomarkers in all twelve partitions. We found 270 multi-gene biomarkers (one per ~11,111 sampled) that always made better predictions than the best single-gene model.

Conclusions: The single-gene hypothesis for breast cancer does not appear to retain its validity in the face of improved statistical models, lower-noise genomic technology and better-powered patient cohorts. These results highlight that it is critical to revisit older hypotheses in the light of newer techniques and datasets.

Keywords: Multi-gene models; Single-gene models; Survival models.

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Competing interests

The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
A summary of the experiment design
Fig. 2
Fig. 2
The distribution of biomarker performance on each of the three models tested in four selected meta-dataset partitions. GeneSIMMS biomarkers are separated according to size and the performance of the single-gene AURKA model is displayed. Highlighted datasets comprise the testing cohort in each partition. The total number of patients in the testing cohort is also provided. The plots for the remaining eight partitions can be found in Additional file 1: Figure S1
Fig. 3
Fig. 3
Concordance correlation coefficients of biomarker performance. CCCs from all partitions are displayed by biomarker type (bars). CCCs were re-calculated for all 924 possible subsets of partitions of size six to obtain the 2.5th - 97.5th percentile ranges (whiskers)
See this image and copyright information in PMC

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