Sigrid Klaar

Bio: Sigrid Klaar is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 14, co-authored 18 publications receiving 3249 citations.

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival

Abstract: Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.

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

Abstract: 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. 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. 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. 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.

The clinical value of somatic TP53 gene mutations in 1,794 patients with breast cancer.

Abstract: To investigate the clinical value of somatic TP53 mutations in breast cancer, we assembled clinical and molecular data on 1,794 women with primary breast cancer with long-term follow-up and whose tumor has been screened for mutation in exons 5 to 8 of TP53 by gene sequencing. TP53 mutations were more frequent in tumors of ductal and medullar types, aggressive phenotype (high grade, large size, node positive cases, and low hormone receptor content) and in women 10 years; P < 0.0001) compared with patients with no such mutation. The prognostic value of TP53 mutation was independent of tumor size, node status, and hormone receptor content, confirming and reconciling previous findings in smaller series. Moreover, an interaction between TP53 mutation and progesterone receptor (PR) status was revealed, TP53 mutation combined with the absence of progesterone receptor being associated with the worst prognosis. Whereas previous studies have emphasized the fact that missense mutations in the DNA-binding motifs have a worse prognosis than missense mutations outside these motifs, we show that non-missense mutations have prognostic value similar to missense mutations in DNA-binding motifs. Nonetheless, specific missense mutants (codon 179 and R248W) seem to be associated with an even worse prognosis. These results, obtained on the largest series analyzed thus far, show that TP53 mutations identified by gene sequencing have an independent prognostic value in breast cancer and could have potential uses in clinical practice.

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients.

Abstract: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability.

The expression of vascular endothelial growth factor correlates with mutant p53 and poor prognosis in human breast cancer.

Abstract: Wild-type p53 protein has been shown to inhibit angiogenesis through thrombospondin in the preclinical setting. Here, we determined the associations between the expression of the angiogenic factor vascular endothelial growth factor (VEGF) and the p53 status, including different mutation sites and types, in primary breast cancer. Cytosols from 224 primary breast cancer patients were analyzed with an enzyme immunoassay for determination of human VEGF165 protein content. p53 status was determined by cDNA-based sequencing of the entire coding region, by immunohistochemistry (IHC), and by a p53 luminometric immunoassay (LIA) method. Statistically significant associations was found between higher VEGF content and non-wild-type p53 status for all methods; sequence-based data (P = 0.0019), IHC data (P = 0.0068), and the LIA method (r = 0.427; P > 0.001). Highest VEGF values were detected in tumors with p53 insertions, deletions, and stop codon mutations (P = 0.0043). Combining p53 status and VEGF content resulted in additional prognostic information, relapse-free survival (RFS; P = 0.0377), overall survival (OS; P = 0.0319), and breast cancer corrected survival (BCCS; P = 0.0292). In multivariate analysis, the relative hazard increased when the VEGF data were added to the p53 status, with a relative hazard of 1.7 for RFS and 3.0 for BCCS, compared with 1.1 for RFS and 1.4 for BCCS among the patients with either high VEGF content or p53 mutation. Higher VEGF content was statistically significantly correlated with a worse outcome for patients with estrogen receptor-positive tumors receiving adjuvant tamoxifen: RFS (P = 0.0471), OS (P = 0.0134), BCCS (P = 0.0064), as well as in multivariate analysis with point estimates of 3.4 and 2.1 for BCCS and RFS, respectively. VEGF expression is related to p53 status in human breast cancer patients. Combining VEGF with p53 status resulted in better prognostic prediction.

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Abstract: Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem–like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted “driver” signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

Abstract: Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for t...

Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update.

Abstract: Purpose To update the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guideline recommendations for human epidermal growth factor receptor 2 (HER2) testing in breast cancer to improve the accuracy of HER2 testing and its utility as a predictive marker in invasive breast cancer.

Recommendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Update

Abstract: Purpose.—To update the American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guideline recommendations for human epidermal growth factor receptor 2 (HER2) testing in b...

An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients.

Abstract: Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.