Comparison of Prognostic Gene Expression signatures for Breast Cancer
Haibe-Kains B, C. Desmedt, G. Bontempi and C. Sotiriou
Background:
During the last years, several groups have identified prognostic gene
expression signatures that consistently outperform traditional clinical
parameters (e.g. tumor size, age, and histological grade) and
guidelines (e.g. Adjuvant! Online). Previous publications reported that
gene signatures exhibited similar classification and performance.
Although these studies yielded promising conclusions, some issues
remained open: (i) the dataset which was considered for these studies
was the one employed also for the identification of some gene sets and
as such it could not be considered as an independent test set
and/or (ii) since some of these signatures were developed on another
platform, the initial algorithms could not be applied due to different
or missing probe sequences and difference in gene expressions
measurement. Therefore, signatures were never compared on an
independent population of untreated breast cancer patients, where
classification was computed using the original algorithms and
microarray platforms.
Patients and methods: We
compared three gene expression signatures, the 70-gene, the 76-gene and
the genomic grade signatures, in terms of predicting time to distant
metastasis (TDM), distant metastasis free survival (DMFS), and overall
survival (OS) for the individual patient. To this end, we used the
previously published TRANSBIG independent validation series of
node-negative untreated primary breast cancer patients. We used
Cramer's V statistic to quantify the strength of the association
between two gene signature classifications, i.e. the low- and high-risk
groups of patients. We estimated hazard ratios between the low- and
high-risk groups, as well as the hazard ratios adjusted for clinical
risk as defined by Adjuvant! Online, using the Cox model. We used the
concordance index to quantify the predictive ability of a survival
model. Standard errors, confidence intervals and p-values for the
concordance index were computed making an assumption of asymptotic
normality. The difference in hazard ratios and concordance indices were
computed using a Student t test for two dependent samples.
Results: The three evaluated
signatures had high rates of concordance in their prediction of
clinical outcome, with the 70-gene and genomic grade signatures showing
the highest rate, 89%, compared to 71% and 78 % for the 76- and the
70-gene signatures, and the 76-gene and genomic grade signatures,
respectively. The three signatures had similar capabilities of
predicting TDM, DMFS, and OS in adding significant prognostic
information to that provided by the classical parameters.
Conclusions: Despite the
difference in development of these signatures and the small overlap in
gene identity, they showed similar prognostic performance, confirming
that these prognostic signatures are of wide clinical relevance.
Moreover, the nature of the genomic grade signature suggested that
proliferation might be the common driving force of several prognostic
signatures.