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1.
BMJ Open Qual ; 9(1)2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-32209595

RESUMO

OBJECTIVE: Medical billing data are an attractive source of secondary analysis because of their ease of use and potential to answer population-health questions with statistical power. Although these datasets have known susceptibilities to biases, the degree to which they can distort the assessment of quality measures such as colorectal cancer screening rates are not widely appreciated, nor are their causes and possible solutions. METHODS: Using a billing code database derived from our institution's electronic health records, we estimated the colorectal cancer screening rate of average-risk patients aged 50-74 years seen in primary care or gastroenterology clinic in 2016-2017. 200 records (150 unscreened, 50 screened) were sampled to quantify the accuracy against manual review. RESULTS: Out of 4611 patients, an analysis of billing data suggested a 61% screening rate, an estimate that matches the estimate by the Centers for Disease Control. Manual review revealed a positive predictive value of 96% (86%-100%), negative predictive value of 21% (15%-29%) and a corrected screening rate of 85% (81%-90%). Most false negatives occurred due to examinations performed outside the scope of the database-both within and outside of our institution-but 21% of false negatives fell within the database's scope. False positives occurred due to incomplete examinations and inadequate bowel preparation. Reasons for screening failure include ordered but incomplete examinations (48%), lack of or incorrect documentation by primary care (29%) including incorrect screening intervals (13%) and patients declining screening (13%). CONCLUSIONS: Billing databases are prone to substantial bias that may go undetected even in the presence of confirmatory external estimates. Caution is recommended when performing population-level inference from these data. We propose several solutions to improve the use of these data for the assessment of healthcare quality.

3.
Clin Pharmacol Ther ; 2020 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-32141068

RESUMO

In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record. Data on over 3 million medication orders from an academic medical center were used to train two machine learning models: a deep learning sequence model and a logistic regression model. Both were compared to a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the electronic health record.

4.
J Clin Invest ; 130(2): 565-574, 2020 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-32011317

RESUMO

Real-world data (RWD) continue to emerge as a new source of clinical evidence. Although the best-known use case of RWD has been in drug regulation, RWD are being generated and used by many other parties, including biopharmaceutical companies, payors, clinical researchers, providers, and patients. In this Review, we describe 21 potential uses for RWD across the spectrum of health care. We also discuss important challenges and limitations relevant to the translation of these data into evidence.

5.
Respirology ; 2019 Dec 17.
Artigo em Inglês | MEDLINE | ID: mdl-31846126

RESUMO

BACKGROUND AND OBJECTIVE: AE-IPF has profound prognostic implications, preceding approximately half of all IPF-related deaths. Despite this clinical significance, there are limited data to guide management decisions. Corticosteroids remain the mainstay of treatment despite a lack of strong supporting evidence and mounting concern that they may be harmful. We assessed the impact of corticosteroid therapy on in-hospital mortality in AE-IPF patients. METHODS: AE-IPF subjects were retrospectively identified in the UCSF medical centre's electronic health records from 1 January 2010 to 1 August 2018 using a code-based algorithm followed by case validation. The relationship between corticosteroid treatment and in-hospital mortality was assessed using a Cox model and a propensity score to control for confounding by indication. Secondary outcomes included hospital readmissions and overall survival. RESULTS: In total, 82 AE-IPF subjects were identified, of whom 37 patients (45%) received corticosteroids. AE-IPF subjects treated with corticosteroids were more likely to require ICU level care and mechanical ventilation. There was no statistically significant association between corticosteroid treatment and in-hospital mortality (propensity score weighted, adjusted HR: 1.31; 95% CI: 0.26-6.55; P = 0.74). Overall survival was reduced in AE-IPF subjects receiving corticosteroids (HR: 6.17; 95% CI: 1.35-28.14; P = 0.019). CONCLUSION: Our study found no evidence that corticosteroid use improves outcomes in IPF patients admitted to the hospital with acute exacerbation. Furthermore, corticosteroid use may contribute to reduced overall survival following an exacerbation. Observational cohort studies using larger real-world cohorts can more definitively assess the relationship between corticosteroid treatment and short-term outcomes in AE-IPF.

6.
Retrovirology ; 16(1): 32, 2019 11 11.
Artigo em Inglês | MEDLINE | ID: mdl-31711503

RESUMO

BACKGROUND: HIV-infected cell lines are widely used to study latent HIV infection, which is considered the main barrier to HIV cure. We hypothesized that these cell lines differ from each other and from cells from HIV-infected individuals in the mechanisms underlying latency. RESULTS: To quantify the degree to which HIV expression is inhibited by blocks at different stages of HIV transcription, we employed a recently-described panel of RT-ddPCR assays to measure levels of 7 HIV transcripts ("read-through," initiated, 5' elongated, mid-transcribed/unspliced [Pol], distal-transcribed [Nef], polyadenylated, and multiply-sliced [Tat-Rev]) in bulk populations of latently-infected (U1, ACH-2, J-Lat) and productively-infected (8E5, activated J-Lat) cell lines. To assess single-cell variation and investigate cellular genes associated with HIV transcriptional blocks, we developed a novel multiplex qPCR panel and quantified single cell levels of 7 HIV targets and 89 cellular transcripts in latently- and productively-infected cell lines. The bulk cell HIV transcription profile differed dramatically between cell lines and cells from ART-suppressed individuals. Compared to cells from ART-suppressed individuals, latent cell lines showed lower levels of HIV transcriptional initiation and higher levels of polyadenylation and splicing. ACH-2 and J-Lat cells showed different forms of transcriptional interference, while U1 cells showed a block to elongation. Single-cell studies revealed marked variation between/within cell lines in expression of HIV transcripts, T cell phenotypic markers, antiviral factors, and genes implicated in latency. Expression of multiply-spliced HIV Tat-Rev was associated with expression of cellular genes involved in activation, tissue retention, T cell transcription, and apoptosis/survival. CONCLUSIONS: HIV-infected cell lines differ from each other and from cells from ART-treated individuals in the mechanisms governing latent HIV infection. These differences in viral and cellular gene expression must be considered when gauging the suitability of a given cell line for future research on HIV. At the same time, some features were shared across cell lines, such as low expression of antiviral defense genes and a relationship between productive infection and genes involved in survival. These features may contribute to HIV latency or persistence in vivo, and deserve further study using novel single cell assays such as those described in this manuscript.

7.
Sci Data ; 6(1): 201, 2019 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-31615985

RESUMO

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

8.
JAMIA Open ; 2(1): 10-14, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31633087

RESUMO

Objectives: Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods: We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results: ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion: ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).

9.
Nat Commun ; 10(1): 3045, 2019 07 10.
Artigo em Inglês | MEDLINE | ID: mdl-31292438

RESUMO

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.


Assuntos
Análise de Dados , Coleta de Dados/métodos , Aprendizado de Máquina não Supervisionado , Pesquisa Biomédica/estatística & dados numéricos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Medicina de Precisão/métodos
10.
Bioinformatics ; 35(21): 4515-4518, 2019 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-31214700

RESUMO

MOTIVATION: Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS: We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION: PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

11.
Nat Med ; 25(5): 792-804, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31068711

RESUMO

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.


Assuntos
Big Data , Diabetes Mellitus Tipo 2/etiologia , Medicina de Precisão/estatística & dados numéricos , Adulto , Idoso , Doenças Cardiovasculares/etiologia , Estudos de Coortes , Exoma , Feminino , Microbioma Gastrointestinal , Humanos , Resistência à Insulina , Estudos Longitudinais , Masculino , Metaboloma , Pessoa de Meia-Idade , Modelos Biológicos , Fatores de Risco , Transcriptoma
12.
JAMA Netw Open ; 2(4): e191851, 2019 04 05.
Artigo em Inglês | MEDLINE | ID: mdl-30977847

RESUMO

Importance: There are limited resources providing postdonation conditions that can occur in living donors (LDs) of solid-organ transplant. Consequently, it is difficult to visualize and understand possible postdonation outcomes in LDs. Objective: To assemble an open access resource that is representative of the demographic characteristics in the US national registry, maintained by the Organ Procurement and Transplantation Network and administered by the United Network for Organ Sharing, but contains more follow-up information to help to examine postdonation outcomes in LDs. Design, Setting, and Participants: Cohort study in which the data for the resource and analyses stemmed from the transplant data set derived from 27 clinical studies from the ImmPort database, which is an open access repository for clinical studies. The studies included data collected from 1963 to 2016. Data from the United Network for Organ Sharing Organ Procurement and Transplantation Network national registry collected from October 1987 to March 2016 were used to determine representativeness. Data analysis took place from June 2016 to May 2018. Data from 20 ImmPort clinical studies (including clinical trials and observational studies) were curated, and a cohort of 11 263 LDs was studied, excluding deceased donors, LDs with 95% or more missing data, and studies without a complete data dictionary. The harmonization process involved the extraction of common features from each clinical study based on categories that included demographic characteristics as well as predonation and postdonation data. Main Outcomes and Measures: Thirty-six postdonation events were identified, represented, and analyzed via a trajectory network analysis. Results: The curated data contained 10 869 living kidney donors (median [interquartile range] age, 39 [31-48] years; 6175 [56.8%] women; and 9133 [86.6%] of European descent). A total of 9558 living kidney donors with postdonation data were analyzed. Overall, 1406 LDs (14.7%) had postdonation events. The 4 most common events were hypertension (806 [8.4%]), diabetes (190 [2.0%]), proteinuria (171 [1.8%]), and postoperative ileus (147 [1.5%]). Relatively few events (n = 269) occurred before the 2-year postdonation mark. Of the 1746 events that took place 2 years or more after donation, 1575 (90.2%) were nonsurgical; nonsurgical conditions tended to occur in the wide range of 2 to 40 years after donation (odds ratio, 38.3; 95% CI, 4.12-1956.9). Conclusions and Relevance: Most events that occurred more than 2 years after donation were nonsurgical and could occur up to 40 years after donation. Findings support the construction of a national registry for long-term monitoring of LDs and confirm the value of secondary reanalysis of clinical studies.


Assuntos
Doação Dirigida de Tecido/estatística & dados numéricos , Doadores Vivos/estatística & dados numéricos , Complicações Pós-Operatórias/epidemiologia , Obtenção de Tecidos e Órgãos/métodos , Adulto , Ensaios Clínicos como Assunto , Diabetes Mellitus/epidemiologia , Diabetes Mellitus/etiologia , Feminino , Seguimentos , Taxa de Filtração Glomerular/fisiologia , Humanos , Hipertensão/epidemiologia , Hipertensão/etiologia , Íleus/epidemiologia , Íleus/etiologia , Transplante de Rim/estatística & dados numéricos , Masculino , Pessoa de Meia-Idade , Proteinúria , Sistema de Registros , Estudos Retrospectivos
13.
JAMA Netw Open ; 2(3): e190606, 2019 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-30874779

RESUMO

Importance: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial intelligence models were capable of forecasting future patient outcomes, they could be used to aid practitioners and patients in prognosticating outcomes or simulating potential outcomes under different treatment scenarios. Objective: To assess the ability of an artificial intelligence system to prognosticate the state of disease activity of patients with rheumatoid arthritis (RA) at their next clinical visit. Design, Setting, and Participants: This prognostic study included 820 patients with RA from rheumatology clinics at 2 distinct health care systems with different electronic health record platforms: a university hospital (UH) and a public safety-net hospital (SNH). The UH and SNH had substantially different patient populations and treatment patterns. The UH has records on approximately 1 million total patients starting in January 2012. The UH data for this study were accessed on July 1, 2017. The SNH has records on 65 000 unique individuals starting in January 2013. The SNH data for the study were collected on February 27, 2018. Exposures: Structured data were extracted from the electronic health record, including exposures (medications), patient demographics, laboratories, and prior measures of disease activity. A longitudinal deep learning model was used to predict disease activity for patients with RA at their next rheumatology clinic visit and to evaluate interhospital performance and model interoperability strategies. Main Outcomes and Measures: Model performance was quantified using the area under the receiver operating characteristic curve (AUROC). Disease activity in RA was measured using a composite index score. Results: A total of 578 UH patients (mean [SD] age, 57 [15] years; 477 [82.5%] female; 296 [51.2%] white) and 242 SNH patients (mean [SD] age, 60 [15] years; 195 [80.6%] female; 30 [12.4%] white) were included in the study. Patients at the UH compared with those at the SNH were seen more frequently (median time between visits, 100 vs 180 days) and were more frequently prescribed higher-class medications (biologics) (364 [63.0%] vs 70 [28.9%]). At the UH, the model reached an AUROC of 0.91 (95% CI, 0.86-0.96) in a test cohort of 116 patients. The UH-trained model had an AUROC of 0.74 (95% CI, 0.65-0.83) in the SNH test cohort (n = 117) despite marked differences in the patient populations. In both settings, baseline prediction using each patients' most recent disease activity score had statistically random performance. Conclusions and Relevance: The findings suggest that building accurate models to forecast complex disease outcomes using electronic health record data is possible and these models can be shared across hospitals with diverse patient populations.


Assuntos
Artrite Reumatoide/diagnóstico , Artrite Reumatoide/epidemiologia , Aprendizado Profundo , Diagnóstico por Computador/métodos , Registros Eletrônicos de Saúde/classificação , Adulto , Idoso , Estudos de Coortes , Feminino , Previsões , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico
14.
J Reprod Immunol ; 132: 16-20, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30852461

RESUMO

PROBLEM: Preterm birth (PTB), or the delivery of an infant prior to 37 weeks of gestation, is a major health concern. Although a variety of social, environmental, and maternal factors have been implicated in PTB, causes of preterm labor have remained largely unknown. There is evidence of effectiveness and safety of influenza vaccination during pregnancy, however fewer studies have looked at vaccination response as an indicator of an innate host response that may be associated with adverse pregnancy outcomes. We carried out a pilot study to analyze the flu vaccine response during pregnancy of women who later deliver preterm or term. METHOD OF STUDY: We performed a secondary analysis of the individual-level data from an influenza vaccination response study (openly available from ImmPort) measured by hemagglutination inhibition assay of 91 pregnant women with term deliveries and 11 women who went on to deliver preterm. Flu vaccination responses for H1N1 and H3N2 influenza strains were compared between term and preterm deliveries. RESULTS: Women who went on to deliver preterm showed a significantly (P < 0.001) greater flu vaccine response for the H1N1 strain than women who delivered at term. The vaccine response for H3N2 was not significantly different between these two groups (P = 0.97). CONCLUSIONS: Although the sample size is limited and additional validation is required, our findings suggest an increased activation of the maternal immune system as shown by the stronger vaccination response to H1N1 in women who subsequently delivered preterm, in comparison to women who delivered at term.

15.
Nat Commun ; 10(1): 917, 2019 02 22.
Artigo em Inglês | MEDLINE | ID: mdl-30796226

RESUMO

Monitoring and ensuring the integrity of data within the clinical trial process is currently not always feasible with the current research system. We propose a blockchain-based system to make data collected in the clinical trial process immutable, traceable, and potentially more trustworthy. We use raw data from a real completed clinical trial, simulate the trial onto a proof of concept web portal service, and test its resilience to data tampering. We also assess its prospects to provide a traceable and useful audit trail of trial data for regulators, and a flexible service for all members within the clinical trials network. We also improve the way adverse events are currently reported. In conclusion, we advocate that this service could offer an improvement in clinical trial data management, and could bolster trust in the clinical research process and the ease at which regulators can oversee trials.


Assuntos
Ensaios Clínicos como Assunto/métodos , Auditoria Médica/métodos , Controle de Qualidade , Coleta de Dados , Assistência à Saúde , Humanos , Estudo de Prova de Conceito
16.
Nat Immunol ; 20(2): 163-172, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30643263

RESUMO

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.


Assuntos
Fibrose Pulmonar Idiopática/imunologia , Pulmão/patologia , Ativação de Macrófagos , Macrófagos Alveolares/imunologia , Animais , Antígenos de Diferenciação Mielomonocítica/genética , Antígenos de Diferenciação Mielomonocítica/imunologia , Antígenos de Diferenciação Mielomonocítica/metabolismo , Bleomicina/imunologia , Receptor 1 de Quimiocina CX3C/genética , Receptor 1 de Quimiocina CX3C/imunologia , Receptor 1 de Quimiocina CX3C/metabolismo , Células Cultivadas , Modelos Animais de Doenças , Feminino , Perfilação da Expressão Gênica/métodos , Humanos , Fibrose Pulmonar Idiopática/patologia , Pulmão/citologia , Pulmão/imunologia , Macrófagos Alveolares/metabolismo , Masculino , Camundongos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Regulação para Cima
18.
Bioinformatics ; 35(7): 1197-1203, 2019 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-30169745

RESUMO

MOTIVATION: Flow cytometry and mass cytometry are widely used to diagnose diseases and to predict clinical outcomes. When associating clinical features with cytometry data, traditional analysis methods require cell gating as an intermediate step, leading to information loss and susceptibility to batch effects. Here, we wish to explore an alternative approach that predicts clinical features from cytometry data without the cell-gating step. We also wish to test if such a gating-free approach increases the accuracy and robustness of the prediction. RESULTS: We propose a novel strategy (CytoDx) to predict clinical outcomes using cytometry data without cell gating. Applying CytoDx on real-world datasets allow us to predict multiple types of clinical features. In particular, CytoDx is able to predict the response to influenza vaccine using highly heterogeneous datasets, demonstrating that it is not only accurate but also robust to batch effects and cytometry platforms. AVAILABILITY AND IMPLEMENTATION: CytoDx is available as an R package on Bioconductor (bioconductor.org/packages/CytoDx). Data and scripts for reproducing the results are available on bitbucket.org/zichenghu_ucsf/cytodx_study_code/downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Testes Diagnósticos de Rotina , Software , Análise de Dados , Testes Diagnósticos de Rotina/métodos , Citometria de Fluxo , Humanos , Vacinas contra Influenza
20.
Pharmacol Rev ; 71(1): 1-19, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30545954

RESUMO

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.


Assuntos
Descoberta de Drogas , Mutação , Neoplasias/genética , Genômica , Humanos , Imunoterapia , Terapia de Alvo Molecular , Neoplasias/terapia , Medicina de Precisão
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