Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures.
BACKGROUND
Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.


RESULTS
We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).


CONCLUSIONS
Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71-9.48; P = 0.001) and 4.08 (95% CI 1.79-9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.
Department of Obstetrics and Gynecology, J. W. Goethe-University, Frankfurt, Germany. t.karn@em.uni-frankfurt.de
Thomas
T
Karn
Lajos
L
Pusztai
Uwe
U
Holtrich
Takayuki
T
Iwamoto
Christine Y
CY
Shiang
Marcus
M
Schmidt
Volkmar
V
Müller
Christine
C
Solbach
Regine
R
Gaetje
Lars
L
Hanker
Andre
A
Ahr
Cornelia
C
Liedtke
Eugen
E
Ruckhäberle
Manfred
M
Kaufmann
Achim
A
Rody
D014408:Biomarkers, Tumor; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D015331:Cohort Studies; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D053208:Kaplan-Meier Estimate; D020360:Neoadjuvant Therapy; D011237:Predictive Value of Tests; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000172:deficiency* / Q000378:metabolism; D011960:Receptors, Estrogen / Q000172:deficiency* / Q000378:metabolism; D011980:Receptors, Progesterone / Q000172:deficiency* / Q000378:metabolism; D015203:Reproducibility of Results
D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone