TY  - JOUR
AB  - Breast cancer, one of the most common cancers in women, is classified by the expression of hormone receptors and the growth factor receptor HER2, which is important for personalised tumour treatment with HER2-targeted therapies. Tumour biopsies are required for histopathological diagnosis of HER2 expression by breast cancer cells but are subject to sampling error. In this study, we present a method for identifying and analysing cancer-derived EVs from plasma for the detection of HER2 expression in breast cancer without the need for additional processing steps. We detected nano-sized particles through an optimised flow cytometry approach that allows for the identification of HER2-expressing EVs and quantification of their HER2 expression levels. In a clinical study of 115 breast cancer patients, this optimised flow cytometric analysis detected a range of 1.3 to 50 × 103 HER2+EVs per µl of plasma. The number of HER2+EVs did not correlate directly with tumour size, grade, or metastasis. However, computational integration of data from the quantification of HER2pos EVs per µl/plasma and their HER2 expression levels on a single EV basis allowed for the reliable identification of HER2 expression levels in tumours. Our results reveal the potential for analysing cancer-derived EVs from plasma for the diagnosis and personalised therapy in breast cancer patients.
AU  - Wilhelm, A.D.*
AU  - Flynn, C.*
AU  - Hammer, E.*
AU  - Rössler, J.
AU  - Haller, B.*
AU  - Napieralski, R.*
AU  - Leuthner, M.*
AU  - Tosheska, S.*
AU  - Knoops, K.*
AU  - Mathew, A.*
AU  - Ciarimboli, G.*
AU  - Kranich, J.*
AU  - Flaskamp, L.*
AU  - King, S.*
AU  - Gevensleben, H.*
AU  - Emslander, Q.*
AU  - Pastucha, A.*
AU  - Reisbeck, M.*
AU  - Rief, L.*
AU  - Bronger, H.*
AU  - Dreyer, T.*
AU  - Bausch, A.R.*
AU  - Pichlmair, A.*
AU  - Brocker, T.*
AU  - Zeidler, R.
AU  - Hammerschmidt, W.
AU  - Piedavent-Salomom, M.*
AU  - López-Iglesias, C.*
AU  - Schricker, G.*
AU  - Haydn, O.*
AU  - Kiechle, M.*
AU  - Grill, S.W.*
AU  - Heeren, R.*
AU  - Knolle, P.A.*
AU  - Wilhelm, O.*
AU  - Höchst, B.*
C1  - 74981
C2  - 57764
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Two-dimensional analysis of plasma-derived extracellular vesicles to determine the HER2 status in breast cancer patients.
JO  - Breast Cancer Res.
VL  - 27
IS  - 1
PB  - Bmc
PY  - 2025
SN  - 1465-5411
ER  - 

TY  - JOUR
AB  - The transcription factor TRPS1 is a context-dependent oncogene in breast cancer. In the mammary gland, TRPS1 activity is restricted to the luminal population and is critical during puberty and pregnancy. Its function in the resting state remains however unclear. To evaluate whether it could be a target for cancer therapy, we investigated TRPS1 function in the healthy adult mammary gland using a conditional ubiquitous depletion mouse model where long-term depletion does not affect fitness. Using transcriptomic approaches, flow cytometry and functional assays, we show that TRPS1 activity is essential to maintain a functional luminal progenitor compartment. This requires the repression of both YAP/TAZ and SRF/MRTF activities. TRPS1 represses SRF/MRTF activity indirectly by modulating RhoA activity. Our work uncovers a hitherto undisclosed function of TRPS1 in luminal progenitors intrinsically linked to mechanotransduction in the mammary gland. It may also provide new insights into the oncogenic functions of TRPS1 as luminal progenitors are likely the cells of origin of many breast cancers.
AU  - Tollot-Wegner, M.*
AU  - Jessen, M.*
AU  - Kim, K.*
AU  - Sanz-Moreno, A.
AU  - Spielmann, N.
AU  - Gailus-Durner, V.
AU  - Fuchs, H.
AU  - Hrabě de Angelis, M.
AU  - von Eyss, B.*
C1  - 70623
C2  - 55540
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - TRPS1 maintains luminal progenitors in the mammary gland by repressing SRF/MRTF activity.
JO  - Breast Cancer Res.
VL  - 26
IS  - 1
PB  - Bmc
PY  - 2024
SN  - 1465-5411
ER  - 

TY  - JOUR
AB  - BACKGROUND: Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients. METHODS: We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15). RESULTS: Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy. CONCLUSIONS: We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.
AU  - Morra, A.*
AU  - Escala-Garcia, M.*
AU  - Beesley, J.*
AU  - Keeman, R.*
AU  - Canisius, S.*
AU  - Ahearn, T.U.*
AU  - Andrulis, I.L.*
AU  - Anton-Culver, H.*
AU  - Arndt, V.*
AU  - Auer, P.L.*
AU  - Augustinsson, A.*
AU  - Beane Freeman, L.E.*
AU  - Becher, H.*
AU  - Beckmann, M.W.*
AU  - Behrens, S.*
AU  - Bojesen, S.E.*
AU  - Bolla, M.K.*
AU  - Brenner, H.*
AU  - Brüning, T.*
AU  - Buys, S.S.*
AU  - Caan, B.*
AU  - Campa, D.*
AU  - Canzian, F.*
AU  - Castelao, J.E.*
AU  - Chang-Claude, J.*
AU  - Chanock, S.J.*
AU  - Cheng, T.D.*
AU  - Clarke, C.L.*
AU  - Colonna, S.V.*
AU  - Couch, F.J.*
AU  - Cox, A.*
AU  - Cross, S.S.*
AU  - Czene, K.*
AU  - Daly, M.B.*
AU  - Dennis, J.*
AU  - Dörk, T.*
AU  - Dossus, L.*
AU  - Dunning, A.M.*
AU  - Dwek, M.*
AU  - Eccles, D.M.*
AU  - Ekici, A.B.*
AU  - Eliassen, A.H.*
AU  - Eriksson, M.*
AU  - Evans, D.G.*
AU  - Fasching, P.A.*
AU  - Flyger, H.*
AU  - Fritschi, L.*
AU  - Gago-Dominguez, M.*
AU  - García-Sáenz, J.A.*
AU  - Giles, G.G.*
AU  - Grip, M.*
AU  - Guénel, P.*
AU  - Gündert, M.
AU  - Hahnen, E.*
AU  - Haiman, C.A.*
AU  - Håkansson, N.*
AU  - Hall, P.*
AU  - Hamann, U.*
AU  - Hart, S.N.*
AU  - Hartikainen, J.M.*
AU  - Hartmann, A.*
AU  - He, W.*
AU  - Hooning, M.J.*
AU  - Hoppe, R.*
AU  - Hopper, J.L.*
AU  - Howell, A.*
AU  - Hunter, D.J.*
AU  - Jäger, A.*
AU  - Jakubowska, A.*
AU  - Janni, W.*
AU  - John, E.M.*
AU  - Jung, A.Y.*
AU  - Kaaks, R.*
AU  - Keupers, M.*
AU  - Kitahara, C.M.*
AU  - Koutros, S.*
AU  - Kraft, P.*
AU  - Kristensen, V.N.*
AU  - Kurian, A.W.*
AU  - Lacey, J.V.*
AU  - Lambrechts, D.*
AU  - Le Marchand, L.*
AU  - Lindblom, A.*
AU  - Linet, M.*
AU  - Luben, R.N.*
AU  - Lubiński, J.*
AU  - Lush, M.*
AU  - Mannermaa, A.*
AU  - Manoochehri, M.*
AU  - Margolin, S.*
AU  - Martens, J.W.M.*
AU  - Martinez, M.E.*
AU  - Mavroudis, D.*
AU  - Michailidou, K.*
AU  - Milne, R.L.*
AU  - Mulligan, A.M.*
AU  - Muranen, T.A.*
AU  - Nevanlinna, H.*
AU  - Newman, W.G.*
AU  - Nielsen, S.F.*
AU  - Nordestgaard, B.G.*
AU  - Olshan, A.F.*
AU  - Olsson, H.*
AU  - Orr, N.*
AU  - Park-Simon, T.W.*
AU  - Patel, A.V.*
AU  - Peissel, B.*
AU  - Peterlongo, P.*
AU  - Plaseska-Karanfilska, D.*
AU  - Prajzendanc, K.*
AU  - Prentice, R.L.*
AU  - Presneau, N.*
AU  - Rack, B.*
AU  - Rennert, G.*
AU  - Rennert, H.S.*
AU  - Rhenius, V.*
AU  - Romero, A.*
AU  - Roylance, R.*
AU  - Ruebner, M.*
AU  - Saloustros, E.*
AU  - Sawyer, E.J.*
AU  - Schmutzler, R.K.*
AU  - Schneeweiss, A.*
AU  - Scott, C.*
AU  - Shah, M.*
AU  - Smichkoska, S.*
AU  - Southey, M.C.*
AU  - Stone, J.*
AU  - Surowy, H.M.*
AU  - Swerdlow, A.J.*
AU  - Tamimi, R.M.*
AU  - Tapper, W.J.*
AU  - Teras, L.R.*
AU  - Terry, M.B.*
AU  - Tollenaar, R.A.E.M.*
AU  - Tomlinson, I.*
AU  - Troester, M.A.*
AU  - Truong, T.*
AU  - Vachon, C.M.*
AU  - Wang, Q.*
AU  - Hurson, A.N.*
AU  - Winqvist, R.*
AU  - Wolk, A.*
AU  - Ziogas, A.*
AU  - Brauch, H.*
AU  - Garcia-Closas, M.*
AU  - Pharoah, P.D.P.*
AU  - Easton, D.F.*
AU  - Chenevix-Trench, G.*
AU  - Schmidt, M.K.*
C1  - 64440
C2  - 51840
TI  - Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment.
JO  - Breast Cancer Res.
VL  - 23
IS  - 1
PY  - 2021
SN  - 1465-5411
ER  - 

TY  - JOUR
AB  - Background Around 15-20% of primary breast cancers are characterized by HER2 protein overexpression and/or HER2 gene amplification. Despite the successful development of anti-HER2 drugs, intrinsic and acquired resistance represents a major hurdle. This study was performed to analyze the RANK pathway contribution in HER2-positive breast cancer and anti-HER2 therapy resistance. Methods RANK and RANKL protein expression was assessed in samples from HER2-positive breast cancer patients resistant to anti-HER2 therapy and treatment-naive patients. RANK and RANKL gene expression was analyzed in paired samples from patients treated with neoadjuvant dual HER2-blockade (lapatinib and trastuzumab) from the SOLTI-1114 PAMELA trial. Additionally, HER2-positive breast cancer cell lines were used to modulate RANK expression and analyze in vitro the contribution of RANK signaling to anti-HER2 resistance and downstream signaling. Results RANK and RANKL proteins are more frequently detected in HER2-positive tumors that have acquired resistance to anti-HER2 therapies than in treatment-naive ones. RANK (but not RANKL) gene expression increased after dual anti-HER2 neoadjuvant therapy in the cohort from the SOLTI-1114 PAMELA trial. Results in HER2-positive breast cancer cell lines recapitulate the clinical observations, with increased RANK expression observed after short-term treatment with the HER2 inhibitor lapatinib or dual anti-HER2 therapy and in lapatinib-resistant cells. After RANKL stimulation, lapatinib-resistant cells show increased NF-kappa B activation compared to their sensitive counterparts, confirming the enhanced functionality of the RANK pathway in anti-HER2-resistant breast cancer. Overactivation of the RANK signaling pathway enhances ERK and NF-kappa B signaling and increases lapatinib resistance in different HER2-positive breast cancer cell lines, whereas RANK loss sensitizes lapatinib-resistant cells to the drug. Our results indicate that ErbB signaling is required for RANK/RANKL-driven activation of ERK in several HER2-positive cell lines. In contrast, lapatinib is not able to counteract the NF-kappa B activation elicited after RANKL treatment in RANK-overexpressing cells. Finally, we show that RANK binds to HER2 in breast cancer cells and that enhanced RANK pathway activation alters HER2 phosphorylation status. Conclusions Our data support a physical and functional link between RANK and HER2 signaling in breast cancer and demonstrate that increased RANK signaling may contribute to the development of lapatinib resistance through NF-kappa B activation. Whether HER2-positive breast cancer patients with tumoral RANK expression might benefit from dual HER2 and RANK inhibition therapy remains to be elucidated.
AU  - Sanz-Moreno, A.
AU  - Palomeras, S.*
AU  - Pedersen, K.*
AU  - Morancho, B.*
AU  - Pascual, T.*
AU  - Galvan, P.*
AU  - Benitez, S.*
AU  - Gomez-Miragaya, J.*
AU  - Ciscar, M.*
AU  - Jimenez, M.*
AU  - Pernas, S.*
AU  - Petit, A.*
AU  - Soler-Monso, M.T.*
AU  - Vinas, G.*
AU  - Alsaleem, M.*
AU  - Rakha, E.A.*
AU  - Green, A.R.*
AU  - Santamaria, P.G.*
AU  - Mulder, C.*
AU  - Lemeer, S.*
AU  - Arribas, J.*
AU  - Prat, A.*
AU  - Puig, T.*
AU  - Gonzalez-Suarez, E.*
C1  - 61808
C2  - 50426
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - RANK signaling increases after anti-HER2 therapy contributing to the emergence of resistance in HER2-positive breast cancer.
JO  - Breast Cancer Res.
VL  - 23
IS  - 1
PB  - Bmc
PY  - 2021
SN  - 1465-5411
ER  - 

TY  - JOUR
AB  - Background: Breast cancer is the most prevalent tumor entity in Li-Fraumeni syndrome. Up to 80% of individuals with a Li-Fraumeni-like phenotype do not harbor detectable causative germline TP53 variants. Yet, no systematic panel analyses for a wide range of cancer predisposition genes have been conducted on cohorts of women with breast cancer fulfilling Li-Fraumeni(-like) clinical diagnostic criteria. Methods: To specifically help explain the diagnostic gap of TP53 wild-type Li-Fraumeni(-like) breast cancer cases, we performed array-based CGH (comparative genomic hybridization) and panel-based sequencing of 94 cancer predisposition genes on 83 breast cancer patients suggestive of Li-Fraumeni syndrome who had previously had negative test results for causative BRCA1, BRCA2, and TP53 germline variants. Results: We identified 13 pathogenic or likely pathogenic germline variants in ten patients and in nine genes, including four copy number aberrations and nine single-nucleotide variants or small indels. Three patients presented as double-mutation carriers involving two different genes each. In five patients (5 of 83; 6% of cohort), we detected causative pathogenic variants in established hereditary breast cancer susceptibility genes (i.e., PALB2, CHEK2, ATM). Five further patients (5 of 83; 6% of cohort) were found to harbor pathogenic variants in genes lacking a firm association with breast cancer susceptibility to date (i.e., Fanconi pathway genes, RECQ family genes, CDKN2A/p14ARF, and RUNX1). Conclusions: Our study details the mutational spectrum in breast cancer patients suggestive of Li-Fraumeni syndrome and indicates the need for intensified research on monoallelic variants in Fanconi pathway and RECQ family genes. Notably, this study further reveals a large portion of still unexplained Li-Fraumeni(-like) cases, warranting comprehensive investigation of recently described candidate genes as well as noncoding regions of the TP53 gene in patients with Li-Fraumeni(-like) syndrome lacking TP53 variants in coding regions.
AU  - Penkert, J.*
AU  - Schmidt, G.*
AU  - Hofmann, W.*
AU  - Schubert, S.*
AU  - Schieck, M.*
AU  - Auber, B.*
AU  - Ripperger, T.*
AU  - Hackmann, K.*
AU  - Sturm, M.*
AU  - Prokisch, H.
AU  - Hille-Betz, U.*
AU  - Mark, D.*
AU  - Illig, T.*
AU  - Schlegelberger, B.*
AU  - Steinemann, D.*
C1  - 54102
C2  - 45313
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Breast cancer patients suggestive of Li-Fraumeni syndrome: Mutational spectrum, candidate genes, and unexplained heredity.
JO  - Breast Cancer Res.
VL  - 20
IS  - 1
PB  - Bmc
PY  - 2018
SN  - 1465-5411
ER  - 

TY  - JOUR
AB  - ABSTRACT: INTRODUCTION: Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk. METHODS: We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). RESULTS: The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). CONCLUSIONS: Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.
AU  - Quante, A.S.
AU  - Whittemore, A.S.*
AU  - Shriver, T.*
AU  - Strauch, K.
AU  - Terry, M.B.*
C1  - 11859
C2  - 30862
TI  - Breast cancer risk assessment across the risk continuum: Genetic and nongenetic risk factors contributing to differential model performance.
JO  - Breast Cancer Res.
VL  - 14
IS  - 6
PB  - Biomed Central
PY  - 2012
SN  - 1465-5411
ER  -