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International Journal of Molecular Sciences, Volume 22; doi:10.3390/ijms22052581

Abstract:
Heterogeneous nuclear ribonucleoprotein K (hnRNPK) transcripts are abundant in estrogen receptor (ER)- or progesterone receptor (PR)-positive breast cancer. However, the biological functions of hnRNPK in the ER-mediated signaling pathway have remained largely unknown. Therefore, this study analyzes the functions of hnRNPK expression in the ER-mediated signaling pathway in breast cancer. We initially evaluated hnRNPK expression upon treatment with estradiol (E2) and ICI 182,780 in the ERα-positive breast carcinoma cell line MCF-7. The results revealed that E2 increased hnRNPK; however, hnRNPK expression was decreased with ICI 182,780 treatment, indicating estrogen dependency. We further evaluated the effects of hnRNPK knockdown in the ER-mediated signaling pathway in MCF-7 cells using small interfering RNAs. The results revealed that hnRNPK knockdown decreased ERα expression and ERα target gene pS2 by E2 treatment. As hnRNPK interacts with several other proteins, we explored the interaction between hnRNPK and ERα, which was demonstrated using immunoprecipitation and proximity ligation assay. Subsequently, we immunolocalized hnRNPK in patients with breast cancer, which revealed that hnRNPK immunoreactivity was significantly higher in ERα-positive carcinoma cells and significantly lower in Ki67-positive or proliferative carcinoma cells. These results indicated that hnRNPK directly interacted with ERα and was involved in the ER-mediated signaling pathway in breast carcinoma. Furthermore, hnRNPK expression could be an additional target of endocrine therapy in patients with ERα-positive breast cancer.
Cosimo Tuena, Mattia Chiappini, Claudia Repetto, Giuseppe Riva
Reference Module in Neuroscience and Biobehavioral Psychology; doi:10.1016/b978-0-12-818697-8.00001-7

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, Julie Martinkova, Maria Teresa Ferretti
Published: 1 January 2020
Huntington Disease, Volume 175, pp 437-448; doi:10.1016/b978-0-444-64123-6.00029-1

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Giulio Perugi, Pietro De Rossi, Andrea Fagiolini, Paolo Girardi, Giuseppe Maina, Gabriele Sani, Alessandro Serretti
International Clinical Psychopharmacology, Volume 34, pp 189-205; doi:10.1097/yic.0000000000000260

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Analytical and Bioanalytical Chemistry, Volume 411, pp 3769-3776; doi:10.1007/s00216-019-01838-7

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Steven A. Buechler, , Yesim Gökmen-Polar
Published: 1 February 2019
Clinical Breast Cancer, Volume 19, pp 17-26.e8; doi:10.1016/j.clbc.2018.07.011

The publisher has not yet granted permission to display this abstract.
, Daphné Borja De Mozota, Stéphanie Gaumond,
Published: 1 October 2017
Cancer Epidemiology, Volume 50, pp 268-271; doi:10.1016/j.canep.2017.08.004

The publisher has not yet granted permission to display this abstract.
Andreas Bösl, Andreas Spitzmüller, Zerina Jasarevic, Stefanie Rauch, Silke Jäger, Felix Offner
Published: 29 August 2017
PLOS ONE, Volume 12; doi:10.1371/journal.pone.0183458

Abstract:
Correct risk assessment of disease recurrence in patients with early breast cancer is critically important to detect patients who may be spared adjuvant chemotherapy. In clinical practice this is increasingly done based on the results of gene expression assays. In the present study we compared the concordance of the 70-gene signature MammaPrint (MP) with the 12 gene assay EndoPredict (EP). Representative tissue of 48 primary tumours was analysed with the MP during routine diagnostic purposes. Corresponding formalin-fixed, paraffin-embedded tissue was thereafter analysed by the EP test. Risk categories of both tests were compared. 41 of 48 tumours could be directly compared by both tests. Of the 17 MP low risk cases, only 9 were considered low risk by EP (53% agreement) and of the 24 MP high risk cases, 18 were high risk by EP (75% agreement). Discrepancies occurred in 14 of 41 cases (34.1%). There was only a weak and non-significant correlation between the MP and EP test with an overall concordance of only 66%. The original therapeutic recommendation was based on the MP and would have been changed in 38% of the patients following EP test results. 4 patients developed distant metastases. The respective tumours of these patients were all classified as high risk by the EP, but only 3 were classified as high risk by the MP. Both tests resulted in different treatment recommendations for a significant proportion of patients and cannot be used interchangeably. The results underscore the urgent need for further comparative analyses of multi-genomic tests to avoid misclassification of disease recurrence risk in breast cancer patients.
Kai-Cheng Hsu,
Published: 14 June 2017
PLOS ONE, Volume 12; doi:10.1371/journal.pone.0179575

Abstract:
Precision medicine considers an individual’s unique physiological characteristics as strongly influential in disease vulnerability and in response to specific therapies. Predicting an individual’s susceptibility to developing an illness, making an accurate diagnosis, maximizing therapeutic effects, and minimizing adverse effects for treatment are essential in precision medicine. We introduced model-based precision medicine optimization approaches, including pathogenesis, biomarker detection, and drug target discovery, for treating presynaptic dopamine overactivity. Three classes of one-hit and two-hit enzyme defects were detected as the causes of disease states by the optimization approach of pathogenesis. The cluster analysis and support vector machine was used to detect optimal biomarkers in order to discriminate the accurate etiology from three classes of disease states. Finally, the fuzzy decision-making method was employed to discover common and specific drug targets for each classified disease state. We observed that more accurate diagnoses achieved higher satisfaction grades and dosed fewer enzyme targets to treat the disease. Furthermore, satisfaction grades for common drugs were lower than for specific ones, but common drugs could simultaneously treat several disease states that had different etiologies.
, , , Gary Foster, Andrea Edginton
International Journal of Pharmacokinetics, Volume 2, pp 125-136; doi:10.4155/ipk-2016-0018

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Personalized Medicine pp 21-29; doi:10.1007/978-3-319-39349-0_2

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Eleri Lloyd Davies
Published: 1 January 2016
Medicine, Volume 44, pp 42-46; doi:10.1016/j.mpmed.2015.10.002

, Mohammad Mehdi Banoei, Brent W. Winston,
Annals of the American Thoracic Society, Volume 12, pp 1278-1287; doi:10.1513/annalsats.201505-279ps

Abstract:
Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.
Keith Noto, Saeed Majidi, Andrea G. Edlow, Heather C. Wick, Diana W. Bianchi,
Journal of Computational Biology, Volume 22, pp 402-413; doi:10.1089/cmb.2014.0155

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Valeria Musella, Maurizio Callari, Eleonora Di Buduo, Manuela Scuro, , , Giampaolo Bianchini, Biagio Paolini, , , et al.
Published: 6 April 2015
PLOS ONE, Volume 10; doi:10.1371/journal.pone.0123194

Abstract:
To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.
BMC Cancer, Volume 15; doi:10.1186/s12885-015-1102-7

Abstract:
Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting. To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity. Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer. We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.
International Journal of Environmental Research and Public Health, Volume 11, pp 10915-10939; doi:10.3390/ijerph111010915

Abstract:
Within the ROAMER project, which aims to provide a Roadmap for Mental Health Research in Europe, a two-stage Delphi survey among 86 European experts was conducted in order to identify research priorities in clinical mental health research. Expert consensus existed with regard to the importance of three challenges in the field of clinical mental health research: (1) the development of new, safe and effective interventions for mental disorders; (2) understanding the mechanisms of disease in order to be able to develop such new interventions; and (3) defining outcomes (an improved set of outcomes, including alternative outcomes) to use for clinical mental health research evaluation. Proposed actions involved increasing the utilization of tailored approaches (personalized medicine), developing blended eHealth/mHealth decision aids/guidance tools that help the clinician to choose between various treatment modalities, developing specific treatments in order to better target comorbidity and (further) development of biological, psychological and psychopharmacological interventions. The experts indicated that addressing these priorities will result in increased efficacy and impact across Europe; with a high probability of success, given that Europe has important strengths, such as skilled academics and a long research history. Finally, the experts stressed the importance of creating funding and coordinated networking as essential action needed in order to target the variety of challenges in clinical mental health research.
, , Emilio C. Campos
Published: 7 October 2014
Current Eye Research, Volume 40, pp 162-175; doi:10.3109/02713683.2014.966847

The publisher has not yet granted permission to display this abstract.
C.A. Drukker, H.C. Van Den Hout, , E. Brain, H. Bonnefoi, , A. Goldhirsch, N. Harbeck, A.H. Honkoop, R.H.T. Koornstra, et al.
Published: 1 April 2014
European Journal of Cancer, Volume 50, pp 1045-1054; doi:10.1016/j.ejca.2014.01.016

Abstract:
Clinical decision-making in patients with early stage breast cancer requires adequate risk estimation by medical oncologists. This survey evaluates the agreement among oncologists on risk estimations and adjuvant systemic treatment (AST) decisions and the impact of adding the 70-gene signature to known clinico-pathological factors. Twelve medical oncologists assessed 37 breast cancer cases (cT1–3N0M0) and estimated their risk of recurrence (high or low) and gave a recommendation for AST. Cases were presented in two written questionnaires sent 4 weeks apart. Only the second questionnaire included the 70-gene signature result. The level of agreement among oncologists in risk estimation (κ = 0.57) and AST recommendation (κ = 0.57) was ‘moderate’ in the first questionnaire. Adding the 70-gene signature result significantly increased the agreement in risk estimation to ‘substantial’ (κ = 0.61), while agreement in AST recommendations remained ‘moderate’ (κ = 0.56). Overall, the proportion of high risk was reduced with 7.4% (range: 6.9–22.9%; p < 0.001) and the proportion of chemotherapy that was recommended was reduced with 12.2% (range: 5.4–29.5%; p < 0.001). Oncologists’ risk estimations and AST recommendations vary greatly. Even though the number of participating oncologists is low, our results underline the need for a better standardisation tool in clinical decision-making, in which integration of the 70-gene signature may be helpful in certain subgroups to provide patients with individualised, but standardised treatment.
Keith Noto, Carla Brodley, Saeed Majidi, , Donna K. Slonim
Transactions on Petri Nets and Other Models of Concurrency XV pp 222-236; doi:10.1007/978-3-319-05269-4_18

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Gilda Schmidt, Jochen Fleckenstein, Ingolf Juhasz-Böss
Info Onkologie, Volume 16, pp 38-41; doi:10.1007/s15004-013-0639-3

Gilda Schmidt, Jochen Fleckenstein, Ingolf Juhasz-Böss
gynäkologie + geburtshilfe, Volume 18, pp 21-25; doi:10.1007/s15013-013-0170-5

BMC Medicine, Volume 11, pp 132-132; doi:10.1186/1741-7015-11-132

Abstract:
The central theme of personalized medicine is the premise that an individual’s unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual’s susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging.
, T. Forcht Dagi, Robert S. Rosenson, Margaret K. Offermann
Current Atherosclerosis Reports, Volume 15; doi:10.1007/s11883-013-0321-0

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, Peter Sinn, Florian Fritzsche, Arthur Von Hochstetter, Aurelia Noske, Peter Schraml, Christoph Tausch, Andreas Trojan, Holger Moch
Published: 7 March 2013
PLOS ONE, Volume 8; doi:10.1371/journal.pone.0058483

Abstract:
Several multigene expression-based tests offering prognostic and predictive information in hormone-receptor positive early breast cancer were established during the last years. These tests provide prognostic information on distant recurrences and can serve as an aid in therapy decisions. We analyzed the recently validated reverse-transcription-quantitative-real-time PCR-based multigene-expression Endopredict (EP)-test on 34 hormone-receptor positive breast-cancer cases and compared the EP scores with the Oncotype DX Recurrence-scores (RS) obtained from the same cancer samples. Formalin-fixed, paraffin-embedded invasive breast-cancer tissues from 34 patients were analyzed by the EP-test. Representative tumor blocks were analyzed with Oncotype DX prior to this study. Tumor tissue was removed from unstained slides, total-RNA was isolated and EP-analysis was performed blinded to Oncotype DX results. Extraction of sufficient amounts of RNA and generation of valid EP-scores were possible for all 34 samples. EP classified 11 patients as low-risk and 23 patients as high-risk. RS Score defined 15 patients as low-risk, 10 patients as intermediate-risk in and 9 patients as high-risk. Major-discrepancy occurred in 6 of 34 cases (18%): Low-risk RS was classified as high-risk by EP in 6 cases. Combining the RS intermediate-risk and high-risk groups to a common group, the concordance between both tests was 76%. Correlation between continuous EP and RS-scores was moderate (Pearson-coefficient: 0.65 (p<0.01). We observed a significant but moderate concordance (76%) and moderate correlation (0.65) between RS and EP Score. Differences in results can be explained by different weighting of biological motives covered by the two tests. Further studies are needed to explore the clinical relevance of discrepant test results with respect of outcome.
Qingchao Qiu, Pengcheng Lu, Yuzhu Xiang, Yu Shyr, Xi Chen, , Daniel Joseph Viox, Alfred L. George,
Published: 29 January 2013
PLOS ONE, Volume 8; doi:10.1371/journal.pone.0054979

Abstract:
Robust transcriptional signatures in cancer can be identified by data similarity-driven meta-analysis of gene expression profiles. An unbiased data integration and interrogation strategy has not previously been available. We implemented and performed a large meta-analysis of breast cancer gene expression profiles from 223 datasets containing 10,581 human breast cancer samples using a novel data similarity-based approach (iterative EXALT). Cancer gene expression signatures extracted from individual datasets were clustered by data similarity and consolidated into a meta-signature with a recurrent and concordant gene expression pattern. A retrospective survival analysis was performed to evaluate the predictive power of a novel meta-signature deduced from transcriptional profiling studies of human breast cancer. Validation cohorts consisting of 6,011 breast cancer patients from 21 different breast cancer datasets and 1,110 patients with other malignancies (lung and prostate cancer) were used to test the robustness of our findings. During the iterative EXALT analysis, 633 signatures were grouped by their data similarity and formed 121 signature clusters. From the 121 signature clusters, we identified a unique meta-signature (BRmet50) based on a cluster of 11 signatures sharing a phenotype related to highly aggressive breast cancer. In patients with breast cancer, there was a significant association between BRmet50 and disease outcome, and the prognostic power of BRmet50 was independent of common clinical and pathologic covariates. Furthermore, the prognostic value of BRmet50 was not specific to breast cancer, as it also predicted survival in prostate and lung cancers. We have established and implemented a novel data similarity-driven meta-analysis strategy. Using this approach, we identified a transcriptional meta-signature (BRmet50) in breast cancer, and the prognostic performance of BRmet50 was robust and applicable across a wide range of cancer-patient populations.
, , Alexander Statnikov, I-Ming Wang, Peggy H. Wong
The AAPS Journal, Volume 15, pp 427-437; doi:10.1208/s12248-012-9447-1

Abstract:
Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.
Érika Maria Monteiro Santos, Quannetta T. Edwards, , Silvia Regina Rogatto, Maria Isabel Waddington Achatz Md,
Published: 7 January 2013
by Wiley
Journal of Nursing Scholarship, Volume 45, pp 43-51; doi:10.1111/j.1547-5069.2012.01465.x

Abstract:
Purpose: The article aims to introduce nurses to how genetics‐genomics is currently integrated into cancer care from prevention to treatment and influencing oncology nursing practice. Organizing Construct: An overview of genetics‐genomics is described as it relates to cancer etiology, hereditary cancer syndromes, epigenetics factors, and management of care considerations. Methods: Peer‐reviewed literature and expert professional guidelines were reviewed to address concepts of genetics‐genomics in cancer care. Findings: Cancer is now known to be heterogeneous at the molecular level, with genetic and genomic factors underlying the etiology of all cancers. Understanding how these factors contribute to the development and treatment of both sporadic and hereditary cancers is important in cancer risk assessment, prevention, diagnosis, treatment, and long‐term management and surveillance. Conclusions: Rapidly developing advances in genetics‐genomics are changing all aspects of cancer care, with implications for nursing practice. Clinical Relevance: Nurses can educate cancer patients and their families about genetic‐genomic advances and advocate for use of evidence‐based genetic‐genomic practice guidelines to reduce cancer risk and improve outcomes in cancer management.
BMC Genomics, Volume 14, pp 854-854; doi:10.1186/1471-2164-14-854

Abstract:
Recently, questions have been raised regarding the ability of animal models to recapitulate human disease at the molecular level. It has also been demonstrated that cellular kinases, individually or as a collective unit (the kinome), play critical roles in regulating complex biology. Despite the intimate relationship between kinases and health, little is known about the variability, consistency and stability of kinome profiles across species and individuals.
, Lesley-Ann Martin,
Published: 20 December 2012
by Wiley
International Journal of Cancer, Volume 133, pp 1-13; doi:10.1002/ijc.27997

Abstract:
The higher incidence of breast cancer in developed countries has been tempered by reductions in mortality, largely attributable to mammographic screening programmes and advances in adjuvant therapy. Optimal systemic management requires consideration of clinical, pathological and biological parameters. Oestrogen receptor alpha (ERα), progesterone receptor (PgR) and human epidermal growth factor receptor 2 (HER2) are established biomarkers evaluated at diagnosis, which identify cardinal subtypes of breast cancer. Their prognostic and predictive utility effectively guides systemic treatment with endocrine, anti‐HER2 and chemotherapy. Hence, accurate and reliable determination remains of paramount importance. However, the goals of personalized medicine and targeted therapies demand further information regarding residual risk and potential benefit of additional treatments in specific circumstances. The need for biomarkers which are fit for purpose, and the demands placed upon them, is therefore expected to increase. Technological advances, in particular high‐throughput global gene expression profiling, have generated multi‐gene signatures providing further prognostic and predictive information. The rational integration of routinely evaluated clinico‐pathological parameters with key indicators of biological activity, such as proliferation markers, also provides a ready opportunity to improve the information available to guide systemic therapy decisions. The additional value of such information and its proper place in patient management is currently under evaluation in prospective clinical trials. Expanding the utility of biomarkers to lower resource settings requires an emphasis on cost effectiveness, quality assurance and possible international variations in tumor biology; the potential for improved clinical outcomes should be justified against logistical and economic considerations.
Published: 1 October 2012
The Lancet, Volume 380, pp 1212-1213; doi:10.1016/s0140-6736(12)60781-8

Marit Synnestvedt, Elin Borgen, Hege G. Russnes, Neena T. Kumar, Ellen Schlichting, Karl-Erik Giercksky, Rolf Kåresen, Jahn M. Nesland, Bjørn Naume
Published: 30 August 2012
Acta Oncologica, Volume 52, pp 91-101; doi:10.3109/0284186x.2012.713508

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Alessia Finotti, Giulia Breveglieri, Monica Borgatti,
Liquid Crystal Colloids pp 3-24; doi:10.1007/978-94-007-1226-3_1

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, , Sebastian Aulmann, , , Carsten Denkert, Josef Rüschoff, ,
The Journal of Molecular Diagnostics, Volume 14, pp 199-205; doi:10.1016/j.jmoldx.2012.01.012

Abstract:
Human epidermal growth factor receptor 2 (HER2, alias ERBB2)-targeted therapy in breast and gastric cancers depends on the reliable assessment of HER2 protein expression and (in equivocal cases) the quantitative evaluation of HER2 gene amplification. Typically, HER2 and centromere 17 gene copy numbers are evaluated using in situ hybridization (ISH) to calculate ratios for which cutoff values dividing nonamplified and amplified cases have been proposed. Although several studies have investigated how laboratory procedures affect diagnostics, a rigorous quantitative assessment of the diagnostic guidelines for data analysis is still missing. Here, we analyze the dependence of the diagnosed HER2/chromosome 17 ratios on i) sample size (evaluated cells), ii) gene/chromosome signal distributions, and iii) the approach used for quotient calculation using Monte Carlo simulations. Our data show that the current recommendation may lead to statistical HER2/CHR17 ratio variations of up to 0.94 and may therefore lead to incorrect HER2 status diagnoses, given the ratio threshold of 2.0 defined by the Food and Drug Administration. Moreover, borderline cases may receive different amplification diagnoses, depending on the ratio calculation approach: Brightfield-silver ISH with aggregated signal counts may underestimate the HER2/CHR17 ratio compared with two-color fluorescence ISH. Our results provide a basis for quantitative rationales behind HER2 diagnostic guidelines that call for increased numbers of evaluated cells and emphasize the importance of well-designed data analysis methods in diagnostic pathology, especially for predictive clinical application.
Ningning Dong, Jing Yu, Chaoying Wang, Xiaohui Zheng, Zheng Wang, Lijun Di, Guohong Song, Budong Zhu, Li Che, Jun Jia, et al.
Journal of Cancer Research and Clinical Oncology, Volume 138, pp 1197-1203; doi:10.1007/s00432-012-1183-5

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Eleri Lloyd Davies
Published: 1 January 2012
Medicine, Volume 40, pp 5-9; doi:10.1016/j.mpmed.2011.09.010

Published: 31 October 2011
Drug Discovery Today, Volume 16, pp 852-861; doi:10.1016/j.drudis.2011.08.006

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Adela Fernández Ortega, Laura Jolis López, Gemma Viñas Villaró, Rafael Villanueva Vázquez, Alicia García Arias, , Sonia González Jiménez, Cristina Saura Manich, Javier Cortés Castán
Advances in Therapy, Volume 28, pp 19-38; doi:10.1007/s12325-011-0033-1

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