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1.
BMC Nephrol ; 24(1): 222, 2023 07 27.
Artículo en Inglés | MEDLINE | ID: mdl-37501175

RESUMEN

BACKGROUND: Acute kidney injury (AKI) is defined as a sudden episode of kidney failure but is known to be under-recognized by healthcare professionals. The Kidney Disease Improving Global Outcome (KDIGO) guidelines have formulated criteria to facilitate AKI diagnosis by comparing changes in plasma creatinine measurements (PCr). To improve AKI awareness, we implemented these criteria as an electronic alert (e-alert), in our electronic health record (EHR) system. METHODS: For every new PCr measurement measured in the University Medical Center Utrecht that triggered the e-alert, we provided the physician with actionable insights in the form of a memo, to improve or stabilize kidney function. Since e-alerts qualify for software as a medical device (SaMD), we designed, implemented and validated the e-alert according to the European Union In Vitro Diagnostic Regulation (IVDR). RESULTS: We evaluated the impact of the e-alert using pilot data six months before and after implementation. 2,053 e-alerts of 866 patients were triggered in the before implementation, and 1,970 e-alerts of 853 patients were triggered after implementation. We found improvements in AKI awareness as measured by (1) 2 days PCr follow up (56.6-65.8%, p-value: 0.003), and (2) stop of nephrotoxic medication within 7 days of the e-alert (59.2-63.2%, p-value: 0.002). CONCLUSION: Here, we describe the design and implementation of the e-alert in line with the IVDR, leveraging a multi-disciplinary team consisting of physicians, clinical chemists, data managers and data scientists, and share our firsts results that indicate an improved awareness among treating physicians.


Asunto(s)
Lesión Renal Aguda , Humanos , Proyectos Piloto , Diagnóstico Precoz , Lesión Renal Aguda/terapia , Pruebas de Función Renal , Centros Médicos Académicos
2.
Blood ; 126(17): 1996-2004, 2015 Oct 22.
Artículo en Inglés | MEDLINE | ID: mdl-26330243

RESUMEN

Patients with multiple myeloma have variable survival and require reliable prognostic and predictive scoring systems. Currently, clinical and biological risk markers are used independently. Here, International Staging System (ISS), fluorescence in situ hybridization (FISH) markers, and gene expression (GEP) classifiers were combined to identify novel risk classifications in a discovery/validation setting. We used the datasets of the Dutch-Belgium Hemato-Oncology Group and German-speaking Myeloma Multicenter Group (HO65/GMMG-HD4), University of Arkansas for Medical Sciences-TT2 (UAMS-TT2), UAMS-TT3, Medical Research Council-IX, Assessment of Proteasome Inhibition for Extending Remissions, and Intergroupe Francophone du Myelome (IFM-G) (total number of patients: 4750). Twenty risk markers were evaluated, including t(4;14) and deletion of 17p (FISH), EMC92, and UAMS70 (GEP classifiers), and ISS. The novel risk classifications demonstrated that ISS is a valuable partner to GEP classifiers and FISH. Ranking all novel and existing risk classifications showed that the EMC92-ISS combination is the strongest predictor for overall survival, resulting in a 4-group risk classification. The median survival was 24 months for the highest risk group, 47 and 61 months for the intermediate risk groups, and the median was not reached after 96 months for the lowest risk group. The EMC92-ISS classification is a novel prognostic tool, based on biological and clinical parameters, which is superior to current markers and offers a robust, clinically relevant 4-group model.


Asunto(s)
Biomarcadores de Tumor/genética , Aberraciones Cromosómicas , Perfilación de la Expresión Génica , Mieloma Múltiple/genética , Mieloma Múltiple/patología , Anciano , Estudios de Cohortes , Femenino , Estudios de Seguimiento , Humanos , Hibridación Fluorescente in Situ , Agencias Internacionales , Masculino , Persona de Mediana Edad , Modelos Teóricos , Mieloma Múltiple/mortalidad , Estadificación de Neoplasias , Pronóstico , Factores de Riesgo , Tasa de Supervivencia
3.
Proc Natl Acad Sci U S A ; 110(13): 5139-44, 2013 Mar 26.
Artículo en Inglés | MEDLINE | ID: mdl-23483055

RESUMEN

Metastasis confronts clinicians with two major challenges: estimating the patient's risk of metastasis and identifying therapeutic targets. Because they are key signal integrators connecting cellular processes to clinical outcome, we aimed to identify transcriptional nodes regulating cancer cell metastasis. Using rodent xenograft models that we previously developed, we identified the transcription factor Fos-related antigen-1 (Fra-1) as a key coordinator of metastasis. Because Fra-1 often is overexpressed in human metastatic breast cancers and has been shown to control their invasive potential in vitro, we aimed to assess the implication and prognostic significance of the Fra-1-dependent genetic program in breast cancer metastasis and to identify potential Fra-1-dependent therapeutic targets. In several in vivo assays in mice, we demonstrate that stable RNAi depletion of Fra-1 from human breast cancer cells strongly suppresses their ability to metastasize. These results support a clinically important role for Fra-1 and the genetic program it controls. We show that a Fra-1-dependent gene-expression signature accurately predicts recurrence of breast cancer. Furthermore, a synthetic lethal drug screen revealed that antagonists of the adenosine receptor A2B (ADORA2B) are preferentially toxic to breast tumor cells expressing Fra-1. Both RNAi silencing and pharmacologic blockade of ADORA2B inhibited filopodia formation and invasive activity of breast cancer cells and correspondingly reduced tumor outgrowth in the lungs. These data show that Fra-1 activity is causally involved in and is a prognostic indicator of breast cancer metastasis. They suggest that Fra-1 activity predicts responsiveness to inhibition of pharmacologically tractable targets, such as ADORA2B, which may be used for clinical interference of metastatic breast cancer.


Asunto(s)
Neoplasias de la Mama/metabolismo , Regulación Neoplásica de la Expresión Génica , Proteínas Proto-Oncogénicas c-fos/metabolismo , Receptor de Adenosina A2B/metabolismo , Antagonistas del Receptor de Adenosina A2/farmacología , Animales , Neoplasias de la Mama/tratamiento farmacológico , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Línea Celular Tumoral , Femenino , Humanos , Ratones , Ratones Endogámicos BALB C , Ratones Desnudos , Invasividad Neoplásica , Metástasis de la Neoplasia , Trasplante de Neoplasias , Proteínas Proto-Oncogénicas c-fos/genética , Seudópodos/genética , Seudópodos/metabolismo , Seudópodos/patología , Ratas , Receptor de Adenosina A2B/genética , Trasplante Heterólogo , Ensayos Antitumor por Modelo de Xenoinjerto
4.
Int J Lab Hematol ; 44(1): 127-134, 2022 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-34448362

RESUMEN

OBJECTIVES: Typically, prognostic capability of gene expression profiling (GEP) is studied in the context of clinical trials, for which 50%-80% of patients are not eligible, possibly limiting the generalizability of findings to routine practice. Here, we evaluate GEP analysis outside clinical trials, aiming to improve clinical risk assessment of multiple myeloma (MM) patients. METHODS: A total of 155 bone marrow samples from MM patients were collected from which RNA was analyzed by microarray. Sixteen previously developed GEP-based markers were evaluated, combined with survival data, and studied using Cox proportional hazard regression. RESULTS: Gene expression profiling-based markers SKY92 and the PR-cluster were shown to be independent prognostic factors for survival, with hazard ratios and 95% confidence interval of 3.6 [2.0-6.8] (P < .001) and 5.8 [2.7-12.7] (P < .01) for overall survival (OS). A multivariate model proved only SKY92 and the PR-cluster to be independent prognostic factors compared to cytogenetic high-risk patients, the International Staging System (ISS), and revised ISS. A substantial number of high-risk individuals could be further identified when SKY92 was added to the cytogenetic, ISS, or R-ISS. In the cytogenetic standard-risk group, ISS I/II, and R-ISS I/II, 13%, 23%, and 23% of patients with adverse survivals were identified. CONCLUSIONS: For the first time, this study confirmed the prognostic value of GEP markers outside clinical trials. Conventional prognostic models to define high-risk MM are improved by the incorporation of GEP markers.


Asunto(s)
Biomarcadores de Tumor , Perfilación de la Expresión Génica , Mieloma Múltiple/genética , Mieloma Múltiple/mortalidad , Transcriptoma , Células de la Médula Ósea/metabolismo , Manejo de la Enfermedad , Humanos , Mieloma Múltiple/diagnóstico , Mieloma Múltiple/terapia , Estadificación de Neoplasias , Variantes Farmacogenómicas , Pronóstico , Modelos de Riesgos Proporcionales , Estudios Retrospectivos
5.
Front Med (Lausanne) ; 8: 793815, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-35211485

RESUMEN

The increased use of electronic health records (EHRs) has improved the availability of routine care data for medical research. Combined with machine learning techniques this has spurred the development of early warning scores (EWSs) in hospitals worldwide. EWSs are commonly used in the hospital where they have been developed, yet few have been transported to external settings and/or internationally. In this perspective, we describe our experiences in implementing the TREWScore, a septic shock EWS, and the transportability challenges regarding domain, predictors, and clinical outcome we faced. We used data of 53,330 ICU stays from Medical Information Mart for Intensive Care-III (MIMIC-III) and 18,013 ICU stays from the University Medical Center (UMC) Utrecht, including 17,023 (31.9%) and 2,557 (14.2%) cases of sepsis, respectively. The MIMIC-III and UMC populations differed significantly regarding the length of stay (6.9 vs. 9.0 days) and hospital mortality (11.6% vs. 13.6%). We mapped all 54 TREWScore predictors to the UMC database: 31 were readily available, seven required unit conversion, 14 had to be engineered, one predictor required text mining, and one predictor could not be mapped. Lastly, we classified sepsis cases for septic shock using the sepsis-2 criteria. Septic shock populations (UMC 31.3% and MIMIC-III 23.3%) and time to shock events showed significant differences between the two cohorts. In conclusion, we identified challenges to transportability and implementation regarding domain, predictors, and clinical outcome when transporting EWS between hospitals across two continents. These challenges need to be systematically addressed to improve model transportability between centers and unlock the potential clinical utility of EWS.

6.
J Mol Diagn ; 23(1): 120-129, 2021 01.
Artículo en Inglés | MEDLINE | ID: mdl-33152501

RESUMEN

Multiple myeloma (MM) is an incurable plasma cell cancer with a large variability in survival. Patients with MM classified as high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. Analytical validation of the SKY92 assay was performed with primary bone marrow specimens from 12 patients with MM and 7 reference cell line specimens. The SKY92 results were 100% concordant with the reference and/or their expected result for sensitivity, specificity, microarray stability, and RLT buffer stability. The SKY92 results were 90% concordant for primary specimen stability, 96.4% concordant for intermediate precision, and 80% to 100% concordant for RNA stability. For the cell-line reproducibility, the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥77.8% and ≥92.9% of experiments for patient specimens and cell lines, respectively. Statistical acceptance thresholds were developed as Δ ≤1.48 (change in SKY92 score) and SD ≤0.45 (SD across SKY92 scores). Using the Clinical and Laboratory Standards Institute method of choice (EP05-A2/A3), restricted maximum likelihood, the observed Δ values (0 to 1.14) and SDs (0.22 to 0.31) passed acceptance criteria. Thus, we successfully present analytical validation for the SKY92 assay as a prognostic molecular test for individual patients with MM.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Regulación Neoplásica de la Expresión Génica , Técnicas de Diagnóstico Molecular/métodos , Mieloma Múltiple/genética , Transcriptoma , Biomarcadores de Tumor/genética , Donantes de Sangre , Estudios de Casos y Controles , Línea Celular Tumoral , Humanos , Mieloma Múltiple/mortalidad , Mieloma Múltiple/patología , Pronóstico , Recurrencia , Reproducibilidad de los Resultados , Medición de Riesgo , Sensibilidad y Especificidad
7.
EJHaem ; 2(3): 375-384, 2021 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-35844693

RESUMEN

Multiple myeloma (MM) is a heterogeneous hematologic malignancy associated with several risk factors including genetic aberrations which impact disease response and survival. Thorough risk classification is essential to select the best clinical strategy to optimize outcomes. The SKY92 molecular signature classifies patients as standard- or high-risk for progression. The PRospective Observational Multiple Myeloma Impact Study (PROMMIS; NCT02911571) measures impact of SKY92 on risk classification and treatment plan. Newly diagnosed MM patients had bone marrow aspirates analyzed for SKY92. Physicians completed a questionnaire for each patient capturing risk classification, hypothetical treatment plan, and physician confidence in the treatment plan, before and after unblinding SKY92. One hundred forty seven MM patients were enrolled. Before unblinding SKY92, physicians regarded 74 (50%) patients as clinical standard-risk. After unblinding SKY92, 16 patients were re-assigned as high-risk by the physician, and for 15 of them treatment strategy was impacted, resulting in an escalated treatment plan. For the 73 (50%) clinical high-risk patients, SKY92 indicated 46 patients to be standard-risk; for 31 of these patients the treatment strategy was impacted consistent with a de-escalation of risk. Overall, SKY92 impacted treatment decisions in 37% of patients (p < 0.001). For clinical decision-making, physicians incorporated SKY92, and the final assigned clinical risk was in line with SKY92 for 89% of patients. Furthermore, SKY92 significantly increased the confidence of the physicians' treatment decisions (p < 0.001). This study shows potential added value of SKY92 in MM for treatment decision making.

8.
Clin Cancer Res ; 26(22): 5952-5961, 2020 11 15.
Artículo en Inglés | MEDLINE | ID: mdl-32913136

RESUMEN

PURPOSE: Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors. EXPERIMENTAL DESIGN: We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures. STLsig uses genetically similar patients who have received an alternative treatment to model which patients will benefit more from proteasome inhibitors than from an alternative treatment. STLsig constructs gene networks by linking genes that are synergistic in their ability to predict benefit. RESULTS: In a dataset of 910 patients with multiple myeloma, STLsig identified two gene networks that together can predict benefit to the proteasome inhibitor, bortezomib. In class "benefit," we found an HR of 0.47 (P = 0.04) in favor of bortezomib, while in class "no benefit," the HR was 0.91 (P = 0.68). Importantly, we observed a similar performance (HR class benefit, 0.46; P = 0.04) in an independent patient cohort. Moreover, this signature also predicts benefit for the proteasome inhibitor, carfilzomib, indicating it is not specific to bortezomib. No equivalent signature can be found when the genes in the signature are excluded from the analysis, indicating that they are essential. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or multiple myeloma disease progression. CONCLUSIONS: STLsig can identify gene signatures that could aid in treatment decisions for patients with multiple myeloma and provide insight into the biological mechanism behind treatment benefit.


Asunto(s)
Redes Reguladoras de Genes/efectos de los fármacos , Terapia Molecular Dirigida , Mieloma Múltiple/tratamiento farmacológico , Inhibidores de Proteasoma/química , Antineoplásicos/química , Antineoplásicos/uso terapéutico , Bortezomib/química , Bortezomib/uso terapéutico , Línea Celular Tumoral , Simulación por Computador , Resistencia a Antineoplásicos/efectos de los fármacos , Sinergismo Farmacológico , Humanos , Aprendizaje Automático , Mieloma Múltiple/patología , Oligopéptidos/química , Oligopéptidos/uso terapéutico , Complejo de la Endopetidasa Proteasomal/química , Complejo de la Endopetidasa Proteasomal/efectos de los fármacos , Inhibidores de Proteasoma/uso terapéutico
9.
Blood Adv ; 4(24): 6298-6309, 2020 12 22.
Artículo en Inglés | MEDLINE | ID: mdl-33351127

RESUMEN

The standard prognostic marker for multiple myeloma (MM) patients is the revised International Staging System (R-ISS). However, there is room for improvement in guiding treatment. This applies particularly to older patients, in whom the benefit/risk ratio is reduced because of comorbidities and subsequent side effects. We hypothesized that adding gene-expression data to R-ISS would generate a stronger marker. This was tested by combining R-ISS with the SKY92 classifier (SKY-RISS). The HOVON-87/NMSG-18 trial (EudraCT: 2007-004007-34) compared melphalan-prednisone-thalidomide followed by thalidomide maintenance (MPT-T) with melphalan-prednisone-lenalidomide followed by lenalidomide maintenance (MPR-R). From this trial, 168 patients with available R-ISS status and gene-expression profiles were analyzed. R-ISS stages I, II, and III were assigned to 8%, 75%, and 7% of patients, respectively (3-year overall survival [OS] rates: 80%, 65%, 33%, P = 8 × 10-3). Using the SKY92 classifier, 13% of patients were high risk (HR) (3-year OS rates: standard risk [SR], 70%; HR, 28%; P < .001). Combining SKY92 with R-ISS resulted in 3 risk groups: SKY-RISS I (SKY-SR + R-ISS-I; 15%), SKY-RISS III (SKY-HR + R-ISS-II/III; 11%), and SKY-RISS II (all other patients; 74%). The 3-year OS rates for SKY-RISS I, II, and III are 88%, 66%, and 26%, respectively (P = 6 × 10-7). The SKY-RISS model was validated in older patients from the CoMMpass dataset. Moreover, SKY-RISS demonstrated predictive potential: HR patients appeared to benefit from MPR-R over MPT-T (median OS, 55 and 14 months, respectively). Combined, SKY92 and R-ISS classify patients more accurately. Additionally, benefit was observed for MPR-R over MPT-T in SKY92-RISS HR patients only.


Asunto(s)
Mieloma Múltiple , Anciano , Humanos , Lenalidomida , Mieloma Múltiple/diagnóstico , Mieloma Múltiple/tratamiento farmacológico , Pronóstico , Talidomida
10.
Eur J Cancer ; 140: 11-18, 2020 11.
Artículo en Inglés | MEDLINE | ID: mdl-33032086

RESUMEN

PURPOSE: Patients with stage I/IIA cutaneous melanoma (CM) are currently not eligible for adjuvant therapies despite uncertainty in relapse risk. Here, we studied the ability of a recently developed model which combines clinicopathologic and gene expression variables (CP-GEP) to identify stage I/IIA melanoma patients who have a high risk for disease relapse. PATIENTS AND METHODS: Archival specimens from a cohort of 837 consecutive primary CMs were used for assessing the prognostic performance of CP-GEP. The CP-GEP model combines Breslow thickness and patient age, with the expression of eight genes in the primary tumour. Our specific patient group, represented by 580 stage I/IIA patients, was stratified based on their risk of relapse: CP-GEP High Risk and CP-GEP Low Risk. The main clinical end-point of this study was five-year relapse-free survival (RFS). RESULTS: Within the stage I/IIA melanoma group, CP-GEP identified a high-risk patient group (47% of total stage I/IIA patients) which had a considerably worse five-year RFS than the low-risk patient group; 74% (95% confidence interval [CI]: 67%-80%) versus 89% (95% CI: 84%-93%); hazard ratio [HR] = 2.98 (95% CI: 1.78-4.98); P < 0.0001. Of patients in the high-risk group, those who relapsed were most likely to do so within the first 3 years. CONCLUSION: The CP-GEP model can be used to identify stage I/IIA patients who have a high risk for disease relapse. These patients may benefit from adjuvant therapy.


Asunto(s)
Expresión Génica/genética , Melanoma/genética , Melanoma/patología , Recurrencia Local de Neoplasia/genética , Recurrencia Local de Neoplasia/patología , Neoplasias Cutáneas/genética , Neoplasias Cutáneas/patología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Estudios de Cohortes , Intervalos de Confianza , Supervivencia sin Enfermedad , Femenino , Perfilación de la Expresión Génica/métodos , Humanos , Masculino , Persona de Mediana Edad , Pronóstico , Modelos de Riesgos Proporcionales , Adulto Joven
11.
JCO Precis Oncol ; 4: 319-334, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32405608

RESUMEN

PURPOSE: More than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here we describe a model that combines clinicopathologic and molecular variables to identify patients with thin and intermediate thickness melanomas who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis. PATIENTS AND METHODS: Genes with functional roles in melanoma metastasis were discovered by analysis of next generation sequencing data and case control studies. We then used PCR to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin and intermediate thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross validation scheme to predict the presence of SLN metastasis from molecular, clinical and histologic variables. RESULTS: Expression of genes with roles in epithelial-to-mesenchymal transition (glia derived nexin, growth differentiation factor 15, integrin ß3, interleukin 8, lysyl oxidase homolog 4, TGFß receptor type 1 and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model which included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age; AUC, 0.82; 95% CI, 0.78-0.86; SLN biopsy reduction rate of 42% at a negative predictive value of 96%. CONCLUSION: A combined model including clinicopathologic and gene expression variables improved the identification of melanoma patients who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis.

12.
BMC Bioinformatics ; 10 Suppl 1: S20, 2009 Jan 30.
Artículo en Inglés | MEDLINE | ID: mdl-19208120

RESUMEN

BACKGROUND: Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition. RESULTS: We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples. CONCLUSION: The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Modelos Lineales , Neoplasias/genética , Animales , Genoma , Humanos , Ratones
13.
BMC Genomics ; 9: 375, 2008 Aug 06.
Artículo en Inglés | MEDLINE | ID: mdl-18684329

RESUMEN

BACKGROUND: Michiels et al. (Lancet 2005; 365: 488-92) employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a 'gold standard'. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories. RESULTS: We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a 'gold standard'. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures. CONCLUSION: The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolation.


Asunto(s)
Neoplasias de la Mama/genética , Biología Computacional , Bases de Datos Genéticas , Perfilación de la Expresión Génica , Validación de Programas de Computación , Humanos , Metaanálisis como Asunto , Distribución Aleatoria , Tamaño de la Muestra , Sesgo de Selección
14.
Breast Cancer Res ; 10(6): R93, 2008.
Artículo en Inglés | MEDLINE | ID: mdl-19014521

RESUMEN

INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.


Asunto(s)
Neoplasias de la Mama/genética , Proliferación Celular , Biología Computacional , Perfilación de la Expresión Génica , Fenómenos del Sistema Inmunológico/fisiología , Empalme del ARN/fisiología , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/patología , Bases de Datos Genéticas , Femenino , Humanos , Pronóstico , Tasa de Supervivencia
15.
Nat Commun ; 9(1): 2943, 2018 07 27.
Artículo en Inglés | MEDLINE | ID: mdl-30054467

RESUMEN

Many cancer treatments are associated with serious side effects, while they often only benefit a subset of the patients. Therefore, there is an urgent clinical need for tools that can aid in selecting the right treatment at diagnosis. Here we introduce simulated treatment learning (STL), which enables prediction of a patient's treatment benefit. STL uses the idea that patients who received different treatments, but have similar genetic tumor profiles, can be used to model their response to the alternative treatment. We apply STL to two multiple myeloma gene expression datasets, containing different treatments (bortezomib and lenalidomide). We find that STL can predict treatment benefit for both; a twofold progression free survival (PFS) benefit is observed for bortezomib for 19.8% and a threefold PFS benefit for lenalidomide for 31.1% of the patients. This demonstrates that STL can derive clinically actionable gene expression signatures that enable a more personalized approach to treatment.


Asunto(s)
Antineoplásicos/uso terapéutico , Bortezomib/uso terapéutico , Lenalidomida/uso terapéutico , Mieloma Múltiple/tratamiento farmacológico , Adulto , Anciano , Algoritmos , Protocolos de Quimioterapia Combinada Antineoplásica , Supervivencia sin Enfermedad , Quimioterapia Combinada , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Femenino , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Humanos , Masculino , Persona de Mediana Edad , Mieloma Múltiple/genética , Pronóstico , Proteínas Ribosómicas/genética , Resultado del Tratamiento
16.
Pharmacogenomics ; 19(3): 213-226, 2018 02.
Artículo en Inglés | MEDLINE | ID: mdl-29334316

RESUMEN

Biomarkers associated with prognosis in multiple myeloma (MM) can be used to stratify patients into risk categories. An attractive alternative to uniform treatment (UT), risk-stratified treatment (RST) is proposed where high-risk patients receive bortezomib-based regimens while standard-risk patients receive alternative less costly regimens. An early Markov-type decision analytic model evaluated the potential therapeutic and economic value of different RST strategies compared with UT in MM patients in key European countries. Results suggest RST strategies were both cheaper and more effective than UT across all countries, with the molecular marker-only strategy RST-SKY92 producing maximum health gains (0.031-0.039 QALYs). The conclusions remained consistent in the univariate sensitivity analyses. These findings should encourage stakeholders to support the adoption of RST approaches in MM.


Asunto(s)
Antineoplásicos/economía , Bortezomib/economía , Costos de la Atención en Salud , Modelos Económicos , Mieloma Múltiple/tratamiento farmacológico , Mieloma Múltiple/economía , Antineoplásicos/uso terapéutico , Biomarcadores de Tumor/análisis , Biomarcadores de Tumor/economía , Bortezomib/uso terapéutico , Análisis Costo-Beneficio , Técnicas de Apoyo para la Decisión , Europa (Continente) , Humanos , Estimación de Kaplan-Meier , Cadenas de Markov , Mieloma Múltiple/mortalidad , Años de Vida Ajustados por Calidad de Vida
17.
Clin Lymphoma Myeloma Leuk ; 17(9): 555-562, 2017 09.
Artículo en Inglés | MEDLINE | ID: mdl-28735890

RESUMEN

BACKGROUND: High risk and low risk multiple myeloma patients follow a very different clinical course as reflected in their PFS and OS. To be clinically useful, methodologies used to identify high and low risk disease must be validated in representative independent clinical data and available so that patients can be managed appropriately. A recent analysis has indicated that SKY92 combined with the International Staging System (ISS) identifies patients with different risk disease with high sensitivity. PATIENTS AND METHODS: Here we computed the performance of eight gene expression based classifiers SKY92, UAMS70, UAMS80, IFM15, Proliferation Index, Centrosome Index, Cancer Testis Antigen and HM19 as well as the combination of SKY92/ISS in an independent cohort of 91 newly diagnosed MM patients. RESULTS: The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. CONCLUSION: Among the eight signatures, SKY92 identified the largest proportion of patients (21%) also with the highest HR (8.2). Our analysis also validated the combination SKY92/ISS for identification of three classes; low risk (42%), intermediate risk (37%) and high risk (21%). Between low risk and high risk classes the HR is >10.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Mieloma Múltiple/diagnóstico , Mieloma Múltiple/genética , Estadificación de Neoplasias/métodos , Adulto , Anciano , Anciano de 80 o más Años , Biomarcadores de Tumor , Estudios de Cohortes , Femenino , Regulación Neoplásica de la Expresión Génica , Humanos , Hibridación Fluorescente in Situ , Estimación de Kaplan-Meier , Masculino , Persona de Mediana Edad , Mieloma Múltiple/mortalidad , Pronóstico , Modelos de Riesgos Proporcionales
18.
Clin Pharmacol Ther ; 77(6): 479-85, 2005 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-15961979

RESUMEN

INTRODUCTION: Cytochrome P450 (CYP) plays a key role in the metabolism of coumarin anticoagulants and nonsteroidal anti-inflammatory drugs (NSAIDs). Because CYP2C9 is a genetically polymorphic enzyme, genetic variability could play an important role in the potential interaction between NSAIDs and coumarins. We investigated whether NSAIDs were associated with overanticoagulation during therapy with coumarins and evaluated the effect of the CYP2C9 polymorphisms on this potential interaction. METHODS: We conducted a population-based cohort study among patients of an anticoagulation clinic who were treated with acenocoumarol or phenprocoumon between April 1, 1991, and May 31, 2003, and whose CYP2C9 status was known. Patients were followed up until an international normalized ratio (INR) of 6.0 or greater was reached or until the end of treatment, death, or the end of the study. Proportional hazards regression analysis was used to estimate the risk of an INR of 6.0 or greater in relation to concomitant use of a coumarin anticoagulant and NSAIDs after adjustment for several potentially confounding factors. To study effect modification by CYP2C9 genotype, stratified analyses were performed for wild-type patients and patients with a variant genotype. RESULTS: Of the 973 patients in the cohort, 415 had an INR of 6.0 or greater. Several NSAIDs increased the risk of overanticoagulation. The risk of overanticoagulation was 2.98 (95% confidence interval, 1.09-7.02) in coumarin-treated patients taking NSAIDs with a CYP2C9*2 allele and 10.8 (95% confidence interval, 2.57-34.6) in those with a CYP2C9*3 allele. CONCLUSIONS: Several NSAIDs were associated with overanticoagulation. For NSAIDs that are known CYP2C9 substrates, this risk was modified by allelic variants of CYP2C9. More frequent INR monitoring of patients taking NSAIDs is warranted.


Asunto(s)
Acenocumarol/metabolismo , Antiinflamatorios no Esteroideos/metabolismo , Anticoagulantes/metabolismo , Hidrocarburo de Aril Hidroxilasas/genética , Fenprocumón/metabolismo , Acenocumarol/efectos adversos , Anciano , Anciano de 80 o más Años , Alelos , Antiinflamatorios no Esteroideos/efectos adversos , Anticoagulantes/efectos adversos , Hidrocarburo de Aril Hidroxilasas/metabolismo , Estudios de Cohortes , Citocromo P-450 CYP2C9 , Interacciones Farmacológicas/genética , Interacciones Farmacológicas/fisiología , Sobredosis de Droga , Femenino , Humanos , Masculino , Persona de Mediana Edad , Fenprocumón/efectos adversos , Polimorfismo Genético
19.
Pharmacogenetics ; 14(1): 27-33, 2004 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-15128048

RESUMEN

Cytochrome P4502C9 (CYP2C9) is the main enzyme implicated in coumarin anticoagulant metabolism. The variant alleles CYP2C9*2 and CYP2C9*3 are associated with an increased response to warfarin. However, an effect on acenocoumarol dose requirements appears to be absent for the CYP2C9*2 allele and the consequences for the metabolism of phenprocoumon have not yet been established. We investigated CYP2C9 polymorphisms in relation to the international normalized ratio (INR) during the first 6 weeks of treatment and its effect on the maintenance dose in a cohort of 1124 patients from the Rotterdam Study who were treated with acenocoumarol or phenprocoumon. There was a statistically significant difference in first INR between patients with variant genotypes and those with the wild-type. Almost all acenocoumarol-treated patients with a variant genotype had a significantly higher mean INR and had a higher risk of an INR > or = 6.0 during the first 6 weeks of treatment. A clear genotype-dose relationship was found for acenocoumarol-treated patients. For patients on phenprocoumon, no significant differences were found between variant genotypes and the wild-type genotype. Individuals with one or more CYP2C9*2 or CYP2C9*3 allele(s) require a significantly lower dose of acenocoumarol compared to wild-type patients. Phenprocoumon appears to be a clinically useful alternative in patients carrying the CYP2C9*2 and *3 alleles.


Asunto(s)
Acenocumarol/administración & dosificación , Anticoagulantes/administración & dosificación , Hidrocarburo de Aril Hidroxilasas/genética , Isoenzimas/genética , Fenprocumón/administración & dosificación , Anciano , Secuencia de Bases , Estudios de Cohortes , Citocromo P-450 CYP2C9 , Cartilla de ADN , Femenino , Genotipo , Humanos , Masculino , Persona de Mediana Edad , Medición de Riesgo
20.
Thromb Haemost ; 92(1): 61-6, 2004 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-15213846

RESUMEN

The principal enzyme involved in coumarin metabolism is CYP2C9. Allelic variants of CYP2C9, CYP2C9*2 and CYP2C9*3, code for enzymes with reduced activity. Despite increasing evidence that patients with these genetic variants require lower maintenance doses of anticoagulant therapy, there is lack of agreement among studies on the risk of bleeding and CYP2C9 polymorphisms. It was, therefore, our objective to study the effect of the CYP2C9 polymorphisms on bleeding complications during initiation and maintenance phases of coumarin anticoagulant therapy. The design of the study was a population-based cohort in a sample of the Rotterdam Study, a study in 7,983 subjects. All patients who started treatment with acenocoumarol or phenprocoumon in the study period from January 1, 1991 through December 31, 1998 and for whom INR data were available were included. Patients were followed until a bleeding complication, the end of their treatment, death or end of the study period. Proportional hazards regression analysis was used to estimate the risk of a bleeding complication in relation to CYP2C9 genotype after adjustment for several potentially confounding factors such as age, gender, target INR level, INR, time between INR measurements, and aspirin use. The effect of variant genotype on bleeding risk was separately examined during the initiation phase of 90 days after starting therapy with coumarins. The 996 patients with analysable data had a mean follow-up time of 481 days (1.3 years); 311 (31.2%) had at least 1 variant CYP2C9 allele and 685 (68.8%) had the wild type genotype. For patients with the wild type genotype, the rate of minor bleeding, major bleeding and fatal bleeding was 15.9, 3.4 and 0.2 per 100 treatment-years, respectively. For patients with a variant genotype, the rate of minor, major and fatal bleeding was 14.6, 5.4 and 0.5 per 100 treatment-years. Patients with a variant genotype on acenocoumarol had a significantly increased risk for a major bleeding event (HR 1.83, 95% CI: 1.01-3.32). During the initiation phase of therapy we found no effect of variant genotype on bleeding risk. In this study among outpatients of an anticoagulation clinic using acenocoumarol or phenprocoumon, having a variant allele of CYP2C9 was associated with an increased risk of major bleeding events in patients on acenocoumarol, but not in patients on phenprocoumon. Although one might consider the assessment of the CYP2C9 genotype of a patient for dose adjustment before starting treatment with acenocoumarol, a prospective randomised trial should demonstrate whether this reduces the increased risk of major bleeding events.


Asunto(s)
Acenocumarol/efectos adversos , Anticoagulantes/efectos adversos , Hidrocarburo de Aril Hidroxilasas/genética , Hemorragia/etiología , Fenprocumón/efectos adversos , Acenocumarol/metabolismo , Anciano , Alelos , Anticoagulantes/metabolismo , Hidrocarburo de Aril Hidroxilasas/metabolismo , Secuencia de Bases , Estudios de Cohortes , Citocromo P-450 CYP2C9 , ADN/genética , Femenino , Genotipo , Hemorragia/enzimología , Hemorragia/genética , Humanos , Masculino , Persona de Mediana Edad , Fenprocumón/metabolismo , Factores de Riesgo
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