Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts.
BACKGROUND
Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction.


METHODS
We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined.


RESULTS
Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston-Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively.


CONCLUSIONS
We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.
Pawitan
Yudi
Y
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Yudi.Pawitan@meb.ki.se
Bjöhle
Judith
J
Amler
Lukas
L
Borg
Anna-Lena
AL
Egyhazi
Suzanne
S
Hall
Per
P
Han
Xia
X
Holmberg
Lars
L
Huang
Fei
F
Klaar
Sigrid
S
Liu
Edison T
ET
Miller
Lance
L
Nordgren
Hans
H
Ploner
Alexander
A
Sandelin
Kerstin
K
Shaw
Peter M
PM
Smeds
Johanna
J
Skoog
Lambert
L
Wedrén
Sara
S
Bergh
Jonas
J
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics* / Q000601:surgery; D017024:Chemotherapy, Adjuvant; D015331:Cohort Studies; D005260:Female; D020869:Gene Expression Profiling; D052138:Genes, Neoplasm; D006801:Humans; D008875:Middle Aged; D009362:Neoplasm Metastasis; D009426:Netherlands; D016017:Odds Ratio; D011379:Prognosis; D016019:Survival Analysis; D013548:Sweden; D016896:Treatment Outcome