Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

Pawitan, Yudi; Bjöhle, Judith; Amler, Lukas C.; Borg, Alexander; Egyházi, Suzanne; Hall, Per; Han, Xia; Holmberg, Lars; Huang, Fei; Klaar, Sigrid; Liu, Edison T.; Miller, Lance D.; Nordgren, Hans; Ploner, Alexander; Sandelin, Kerstin; Shaw, Peter M.; Smeds, Johanna; Skoog, Lambert; Wedrén, Sara; Bergh, Jonas

doi:10.1186/bcr1325

Cited by 680 publications

(591 citation statements)

References 43 publications

(31 reference statements)

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“…We included 238 of our own samples (datasets Frankfurt, Frankfurt-2, and Frankfurt-3) which have been described previously (Ahr et al 2002 [9], [10], Rody et al 2008 [11], Ruckhäberle et al 2008 [12], and Rody et al 2007 [13], respectively) as well as 2792 samples from 22 different publicly available datasets (Table 1): Rotterdam [14][15][16], Mainz [17], Trans-BIG [18], Oxford-Untreated [19], London [20], London-2 [21], Oxford-Tamoxifen, Veridex-Tam [22], Stockholm [23], Uppsala [24,25], San Francisco [26], New York [27], MDA133 [28], EORTC [29], Edinburgh [30], ExpO [31], Signapore [32], Genentech [33], Boston [34], Berlin [35], Paris [36], and Tampa [37]. For comparability, only the ProbeSets from the Affymetrix HG-U133A microarray were used from seven datasets where HG-U133?…”

Section: Methodsmentioning

confidence: 99%

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Karn

Metzler

Ruckhäberle

et al. 2009

Breast Cancer Res Treat

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Section: Methodsmentioning

confidence: 99%

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Karn

Metzler

Ruckhäberle

et al. 2009

Breast Cancer Res Treat

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“…This cohort has been published previously. 24,25 The cohort originally included 159 cases of whom 84 had PAM50 data and sufficient paraffin-embedded tumor tissue for glass slide sectioning available, the latter enabling manual scoring of Ki67 by a board-certified pathologist and scanning for DIA (Table 1). A subgroup of 41 tumors classified into Luminal A and Luminal B subtypes was assessed by two additional board-certified pathologists for a brief analysis of interobserver concordance.…”

Section: Patients and Samplesmentioning

confidence: 99%

Digital image analysis outperforms manual biomarker assessment in breast cancer

Stålhammar

Martínez

Lippert³

et al. 2016

Modern Pathology

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145

116

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In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n = 436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ 2 (LR χ 2 ) was higher for DIA that also added significantly more prognostic information to the manual scores (LR− Δχ 2 ). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.

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“…Several research groups have recently used gene expression profiling to define subgroups of tumours associated with good or poor outcome [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. One of these gene expression profiles predicting breast cancer recurrence is the 70-gene prognosis signature [10].…”

Section: Introductionmentioning

confidence: 99%

Validation of 70-gene prognosis signature in node-negative breast cancer

Bueno-de-Mesquita

Linn

Keijzer

et al. 2008

Breast Cancer Res Treat

149

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International audienceThe 70-gene prognosis signature (van't Veer et al., Nature 415(6871):530–536, 2002) may improve the selection of lymph node-negative breast cancer patients for adjuvant systemic therapy. Optimal validation of prognostic classifiers is of great importance and we therefore wished to evaluate the prognostic value of the 70-gene prognosis signature in a series of relatively recently diagnosed lymph node negative breast cancer patients. We evaluated the 70-gene prognosis signature in an independent representative series of patients with invasive breast cancer ( = 123; <55 years; pT1-2N0; diagnosed between 1996 and 1999; median follow-up 5.8 years) by classifying these patients as having a good or poor prognosis signature. In addition, we updated the follow-up of the node-negative patients of the previously published validation-series (Van de Vijver et al., N Engl J Med 347(25):1999–2009, 2002; = 151; median follow-up 10.2 years). The prognostic value of the 70-gene prognosis signature was compared with that of four commonly used clinicopathological risk indexes. The endpoints were distant metastasis (as first event) free percentage (DMFP) and overall survival (OS). The 5-year OS was 82 ± 5% in poor (48%) and 97 ± 2% in good prognosis signature (52%) patients (HR 3.4; 95% CI 1.2–9.6; = 0.021). The 5-years DMFP was 78 ± 6% in poor and 98 ± 2% in good prognosis signature patients (HR 5.7; 95% CI 1.6–20; = 0.007). In the updated series ( = 151; 60% poor vs. 40% good), the 10-year OS was 51 ± 5% and 94 ± 3% (HR 10.7; 95% CI 3.9–30; < 0.01), respectively. The DMFP was 50 ± 6% in poor and 86 ± 5% in good prognosis signature patients (HR 5.5; 95% CI 2.5–12; < 0.01). In multivariate analysis, the prognosis signature was a strong independent prognostic factor in both series, outperforming the clinicopathological risk indexes. The 70-gene prognosis signature is also an independent prognostic factor in node-negative breast cancer patients for women diagnosed in recent years

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Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

Cited by 680 publications

References 43 publications

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Data driven derivation of cutoffs from a pool of 3,030 Affymetrix arrays to stratify distinct clinical types of breast cancer

Digital image analysis outperforms manual biomarker assessment in breast cancer

Validation of 70-gene prognosis signature in node-negative breast cancer

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