Publications

2015
McCoy TH, Castro VM, Rosenfield HR, Cagan A, Kohane IS, Perlis RH. A clinical perspective on the relevance of research domain criteria in electronic health records. Am J PsychiatryAm J Psychiatry. 2015;172 :316-20.Abstract
OBJECTIVE: The limitations of the DSM nosology for capturing dimensionality and overlap in psychiatric syndromes, and its poor correspondence to underlying neurobiology, have been well established. The Research Domain Criteria (RDoC), a proposed dimensional model of psychopathology, may offer new insights into psychiatric illness. For psychiatric clinicians, however, because tools for capturing these domains in clinical practice have not yet been established, the relevance and means of transition from the categorical system of DSM-5 to the dimensional models of RDoC remains unclear. The authors explored a method of extracting these dimensions from existing electronic health record (EHR) notes. METHOD: The authors used information retrieval and natural language processing methods to extract estimates of the RDoC dimensions in the EHRs of a large health system. They parsed and scored EHR documentation for 2,484 admissions covering 2,010 patients admitted to a psychiatric inpatient unit between 2011 and 2013. These domain scores were compared with DSM-IV-based ICD-9 codes to assess face validity. As a measure of predictive validity, these scores were examined for association with two outcomes: length of hospital stay and time to all-cause hospital readmission. Together, these analyses were intended to address the extent to which RDoC symptom domains might capture clinical features already available in narrative notes but not reflected in DSM diagnoses. RESULTS: In mixed-effects models, loadings for the RDoC cognitive and arousal domains were associated with length of hospital stay, while the negative valence and social domains were associated with hazard of all-cause hospital readmission. CONCLUSIONS: These findings show that a computationally derived tool based on RDoC workgroup reports identifies symptom distributions in clinician notes beyond those captured by ICD-9 codes, and these domains have significant predictive validity. More generally, they point to the possibility that clinicians already document RDoC-relevant symptoms, albeit not in a quantified form.
Liao KP, Cai T, Savova GK, Murphy SN, Karlson EW, Ananthakrishnan AN, Gainer VS, Shaw SY, Xia Z, Szolovits P, et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJBMJBMJ. 2015;350 :h1885.
Mandl KD, Kohane IS. Federalist principles for healthcare data networks. Nat BiotechnolNat BiotechnolNature Biotechnology. 2015;33 :360-3.Abstract
Applying federalist principles to networked health record data could facilitate realization of the potential of shared health data.
Ni Y, Wright J, Perentesis J, Lingren T, Deleger L, Kaiser M, Kohane I, Solti I. Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients. BMC Med Inform Decis MakBMC medical informatics and decision makingBMC medical informatics and decision making. 2015;15 :28.Abstract
BACKGROUND: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. METHODS: We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. RESULTS: Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). CONCLUSION: By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.
Wang F, Remke M, Bhat K, Wong ET, Zhou S, Ramaswamy V, Dubuc A, Fonkem E, Salem S, Zhang H, et al. A microRNA-1280/JAG2 network comprises a novel biological target in high-risk medulloblastoma. Oncotarget. 2015;6 :2709-24.Abstract
Over-expression of PDGF receptors (PDGFRs) has been previously implicated in high-risk medulloblastoma (MB) pathogenesis. However, the exact biological functions of PDGFRalpha and PDGFRbeta signaling in MB biology remain poorly understood. Here, we report the subgroup specific expression of PDGFRalpha and PDGFRbeta and their associated biological pathways in MB tumors. c-MYC, a downstream target of PDGFRbeta but not PDGFRalpha, is involved in PDGFRbeta signaling associated with cell proliferation, cell death, and invasion. Concurrent inhibition of PDGFRbeta and c-MYC blocks MB cell proliferation and migration synergistically. Integrated analysis of miRNA and miRNA targets regulated by both PDGFRbeta and c-MYC reveals that increased expression of JAG2, a target of miR-1280, is associated with high metastatic dissemination at diagnosis and a poor outcome in MB patients. Our study may resolve the controversy on the role of PDGFRs in MB and unveils JAG2 as a key downstream effector of a PDGFRbeta-driven signaling cascade and a potential therapeutic target.
Vassy JL, McLaughlin HL, MacRae CA, Seidman CE, Lautenbach D, Krier JB, Lane WJ, Kohane IS, Murray MF, McGuire AL, et al. A one-page summary report of genome sequencing for the healthy adult. Public Health GenomicsPublic Health GenomicsPublic Health Genomics. 2015;18 :123-9.Abstract
As genome sequencing technologies increasingly enter medical practice, genetics laboratories must communicate sequencing results effectively to nongeneticist physicians. We describe the design and delivery of a clinical genome sequencing report, including a one-page summary suitable for interpretation by primary care physicians. To illustrate our preliminary experience with this report, we summarize the genomic findings from 10 healthy participants in a study of genome sequencing in primary care.
Roger VL, Boerwinkle E, Crapo JD, Douglas PS, Epstein JA, Granger CB, Greenland P, Kohane I, Psaty BM. Roger et al. respond to "future of population studies". Am J EpidemiolAm J Epidemiol. 2015;181 :372-3.
Roger VL, Boerwinkle E, Crapo JD, Douglas PS, Epstein JA, Granger CB, Greenland P, Kohane I, Psaty BM. Strategic transformation of population studies: recommendations of the working group on epidemiology and population sciences from the National Heart, Lung, and Blood Advisory Council and Board of External Experts. Am J EpidemiolAm J Epidemiol. 2015;181 :363-8.Abstract
In 2013, the National Heart, Lung, and Blood Institute assembled a working group on epidemiology and population sciences from its Advisory Council and Board of External Experts. The working group was charged with making recommendations to the National Heart, Lung, and Blood Advisory Council about how the National Heart, Lung, and Blood Institute could take advantage of new scientific opportunities and delineate future directions for the epidemiology of heart, lung, blood, and sleep diseases. Seven actionable recommendations were proposed for consideration. The themes included 1) defining the compelling scientific questions and challenges in population sciences and epidemiology of heart, lung, blood, and sleep diseases; 2) developing methods and training mechanisms to integrate "big data" science into the practice of epidemiology; 3) creating a cohort consortium and inventory of major studies to optimize the efficient use of data and specimens; and 4) fostering a more open, competitive approach to evaluating large-scale longitudinal epidemiology and population studies. By building on the track record of success of the heart, lung, blood, and sleep cohorts to leverage new data science opportunities and encourage broad research and training partnerships, these recommendations lay a strong foundation for the transformation of heart, lung, blood, and sleep epidemiology.
Kong SW, Lee IH, Leshchiner I, Krier J, Kraft P, Rehm HL, Green RC, Kohane IS, MacRae CA. Summarizing polygenic risks for complex diseases in a clinical whole-genome report. Genet MedGenet MedGenetics in medicine : official journal of the American College of Medical Genetics. 2015;17 :536-44.Abstract
PURPOSE: Disease-causing mutations and pharmacogenomic variants are of primary interest for clinical whole-genome sequencing. However, estimating genetic liability for common complex diseases using established risk alleles might one day prove clinically useful. METHODS: We compared polygenic scoring methods using a case-control data set with independently discovered risk alleles in the MedSeq Project. For eight traits of clinical relevance in both the primary-care and cardiomyopathy study cohorts, we estimated multiplicative polygenic risk scores using 161 published risk alleles and then normalized them using the population median estimated from the 1000 Genomes Project. RESULTS: Our polygenic score approach identified the overrepresentation of independently discovered risk alleles in cases as compared with controls using a large-scale genome-wide association study data set. In addition to normalized multiplicative polygenic risk scores and rank in a population, the disease prevalence and proportion of heritability explained by known common risk variants provide important context in the interpretation of modern multilocus disease risk models. CONCLUSION: Our approach in the MedSeq Project demonstrates how complex trait risk variants from an individual genome can be summarized and reported for the general clinician and also highlights the need for definitive clinical studies to obtain reference data for such estimates and to establish clinical utility.Genet Med 17 7, 536-544.
Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Cai T. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. J Am Med Inform AssocJ Am Med Inform Assoc. 2015.Abstract
OBJECTIVE: Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. MATERIALS AND METHODS: Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. RESULTS: The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. DISCUSSION: Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. CONCLUSION: The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.
Kohane IS. An Autism Case History to Review the Systematic Analysis of Large-Scale Data to Refine the Diagnosis and Treatment of Neuropsychiatric Disorders. Biol PsychiatryBiological psychiatryBiological psychiatry. 2015;77 :59-65.Abstract
Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. Described here is an analysis of three large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. First is the biomedical literature that highlights the fact that there are two very successful but quite separate research communities and findings pertaining to genetics and the molecular biology of ASD. There are those studies positing ASD causes that are related to immunological dysregulation and those related to disorders of synaptic function and neuronal connectivity. Second is the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD. These systems reveal subsets of patients with distinct clinical trajectories, some of which are immunologically related and others which follow pathologies conventionally thought of as neurological. The third is genome-wide genomic and transcriptomic analyses which show molecular pathways that overlap neurological and immunological mechanisms. The convergence of these three large-scale data perspectives illustrates the scientific leverage that large-scale data analyses can provide in guiding researchers in an approach to the diagnosis of neuropsychiatric disease that is inclusive and comprehensive.
Ananthakrishnan AN, Cagan A, Cai T, Gainer VS, Shaw SY, Churchill S, Karlson EW, Murphy SN, Kohane I, Liao KP. Colonoscopy is associated with a reduced risk for colon cancer and mortality in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol. 2015;13 :322-329 e1.Abstract
BACKGROUND & AIMS: Crohn's disease and ulcerative colitis are associated with an increased risk of colorectal cancer (CRC). Surveillance colonoscopy is recommended at 2- to 3-year intervals beginning 8 years after diagnosis of inflammatory bowel disease (IBD). However, there have been no reports of whether colonoscopy examination reduces the risk for CRC in patients with IBD. METHODS: In a retrospective study, we analyzed data from 6823 patients with IBD (2764 with a recent colonoscopy, 4059 without a recent colonoscopy) seen and followed up for at least 3 years at 2 tertiary referral hospitals in Boston, Massachusetts. The primary outcome was diagnosis of CRC. We examined the proportion of patients undergoing a colonoscopy within 36 months before a diagnosis of CRC or at the end of the follow-up period, excluding colonoscopies performed within 6 months before a diagnosis of CRC, to avoid inclusion of prevalent cancers. Multivariate logistic regression was performed, adjusting for plausible confounders. RESULTS: A total of 154 patients developed CRC. The incidence of CRC among patients without a recent colonoscopy (2.7%) was significantly higher than among patients with a recent colonoscopy (1.6%) (odds ratio [OR], 0.56; 95% confidence interval [CI], 0.39-0.80). This difference persisted in multivariate analysis (OR, 0.65; 95% CI, 0.45-0.93) and was robust when adjusted for a range of assumptions in sensitivity analyses. Among patients with CRC, a colonoscopy within 6 to 36 months before diagnosis was associated with a reduced mortality rate (OR, 0.34; 95% CI, 0.12-0.95). CONCLUSIONS: Recent colonoscopy (within 36 months) is associated with a reduced incidence of CRC in patients with IBD, and lower mortality rates in those diagnosed with CRC.
Ananthakrishnan AN, Cagan A, Cai T, Gainer VS, Shaw SY, Churchill S, Karlson EW, Murphy SN, Kohane I, Liao KP. Diabetes and the risk of infections with immunomodulator therapy in inflammatory bowel diseases. Aliment Pharmacol Ther. 2015;41 :1141-8.Abstract
BACKGROUND: Infections are an important concern in patients using immunosuppressive therapy for their inflammatory bowel disease (IBD). Diabetes affects nearly 10% of Americans. Whether it confers an additional risk with immunosuppression in IBD has not been examined previously. AIM: To examine the association between diabetes and infections with immunomodulator use in IBD METHODS: Using a validated, multi-institutional IBD cohort, we identified all patients who received at least one prescription for immunomodulators (thiopurines, methotrexate). Our primary outcome was infection within 1 year of the prescription of the immunomodulator. Multivariable logistic regression adjusting for relevant confounders was used to estimate the independent association with diabetes. RESULTS: Our study included 2766 patients receiving at least one prescription for immunomodulators among whom 210 (8%) developed an infection within 1 year. Patients who developed an infection were likely to be older, have more comorbidities, more likely to have received a prescription for steroids but similar in initiation of anti-TNF therapy within that year. Only 8% of those without an infection had diabetes compared to 19% of those who developed an infection within 1 year [odds ratio (OR) 2.74, 95% confidence interval (CI) 1.88-3.98, P < 0.001]. On multivariate analysis, diabetes was independently associated with a nearly two-fold increase in risk of infections (OR: 1.80, 95% CI: 1.20-2.68). There was no increase in risk of infections with addition of anti-TNF therapy (OR: 1.14, 95% CI: 0.80-1.63). CONCLUSION: Diabetes is an independent risk factor for infection in IBD patients using immunomodulator therapy.
Ananthakrishnan AN, Cheng A, Cagan A, Cai T, Gainer VS, Shaw SY, Churchill S, Karlson EW, Murphy SN, Kohane I, et al. Mode of childbirth and long-term outcomes in women with inflammatory bowel diseases. Dig Dis Sci. 2015;60 :471-7.Abstract
INTRODUCTION: Inflammatory bowel diseases [IBD; Crohn's disease (CD), ulcerative colitis] often affect women in their reproductive years. Few studies have analyzed the impact of mode of childbirth on long-term IBD outcomes. METHODS: We used a multi-institutional IBD cohort to identify all women in the reproductive age-group with a diagnosis of IBD prior to pregnancy. We identified the occurrence of a new diagnosis code for perianal complications, IBD-related hospitalization and surgery, and initiation of medical therapy after either a vaginal delivery or caesarean section (CS). Cox proportional hazards models adjusting for potential confounders were used to estimate independent effect of mode of childbirth on IBD outcomes. RESULTS: Our cohort included 360 women with IBD (161 CS). Women in the CS group were likely to be older and more likely to have complicated disease behavior prior to pregnancy. During follow-up, there was no difference in the likelihood of IBD-related surgery (multivariate hazard ratio 1.75, 95 % confidence interval (CI) 0.40-7.75), IBD-related hospitalization (HR 1.39), initiation of immunomodulator therapy (HR 1.45), or anti-TNF therapy (HR 1.11). Among the 133 CD pregnancies with no prior perianal disease, we found no excess risk of subsequent new diagnosis perianal fistulae with vaginal delivery compared to CS (HR 0.19, 95 % CI 0.04-1.05). CONCLUSIONS: Mode of delivery did not influence natural history of IBD. In our cohort, vaginal delivery was not associated with increased risk of subsequent perianal disease in women with CD.
Clements CC, Castro VM, Blumenthal SR, Rosenfield HR, Murphy SN, Fava M, Erb JL, Churchill SE, Kaimal AJ, Doyle AE, et al. Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system. Mol PsychiatryMolecular psychiatryMolecular psychiatry. 2015;20 :727-34.Abstract
Previous studies suggested that risk for Autism Spectrum Disorder (ASD) may be increased in children exposed to antidepressants during the prenatal period. The disease specificity of this risk has not been addressed and the possibility of confounding has not been excluded. Children with ASD or attention-deficit hyperactivity disorder (ADHD) delivered in a large New England health-care system were identified from electronic health records (EHR), and each diagnostic group was matched 1:3 with children without ASD or ADHD. All children were linked with maternal health data using birth certificates and EHRs to determine prenatal medication exposures. Multiple logistic regression was used to examine association between prenatal antidepressant exposures and ASD or ADHD risk. A total of 1377 children diagnosed with ASD and 2243 with ADHD were matched with healthy controls. In models adjusted for sociodemographic features, antidepressant exposure prior to and during pregnancy was associated with ASD risk, but risk associated with exposure during pregnancy was no longer significant after controlling for maternal major depression (odds ratio (OR) 1.10 (0.70-1.70)). Conversely, antidepressant exposure during but not prior to pregnancy was associated with ADHD risk, even after adjustment for maternal depression (OR 1.81 (1.22-2.70)). These results suggest that the risk of autism observed with prenatal antidepressant exposure is likely confounded by severity of maternal illness, but further indicate that such exposure may still be associated with ADHD risk. This risk, modest in absolute terms, may still be a result of residual confounding and must be balanced against the substantial consequences of untreated maternal depression.
Diogo D, Bastarache L, Liao KP, Graham RR, Fulton RS, Greenberg JD, Eyre S, Bowes J, Cui J, Lee A, et al. TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits. PLoS OnePLoS ONEPLoS ONE. 2015;10 :e0122271.Abstract
Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits. First, we combined Immunochip dense genotyping (n = 23,092 case/control samples), Exomechip genotyping (n = 18,409 case/control samples) and targeted exon-sequencing (n = 2,236 case/controls samples) to demonstrate that three protein-coding variants in TYK2 (tyrosine kinase 2) independently protect against RA: P1104A (rs34536443, OR = 0.66, P = 2.3 x 10(-21)), A928V (rs35018800, OR = 0.53, P = 1.2 x 10(-9)), and I684S (rs12720356, OR = 0.86, P = 4.6 x 10(-7)). Second, we show that the same three TYK2 variants protect against systemic lupus erythematosus (SLE, Pomnibus = 6 x 10(-18)), and provide suggestive evidence that two of the TYK2 variants (P1104A and A928V) may also protect against inflammatory bowel disease (IBD; P(omnibus) = 0.005). Finally, in a phenome-wide association study (PheWAS) assessing >500 phenotypes using electronic medical records (EMR) in >29,000 subjects, we found no convincing evidence for association of P1104A and A928V with complex phenotypes other than autoimmune diseases such as RA, SLE and IBD. Together, our results demonstrate the role of TYK2 in the pathogenesis of RA, SLE and IBD, and provide supporting evidence for TYK2 as a promising drug target for the treatment of autoimmune diseases.
Castro VM, Minnier J, Murphy SN, Kohane I, Churchill SE, Gainer V, Cai T, Hoffnagle AG, Dai Y, Block S, et al. Validation of electronic health record phenotyping of bipolar disorder cases and controls. Am J PsychiatryAm J Psychiatry. 2015;172 :363-72.Abstract
OBJECTIVE: The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. METHOD: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses was calculated against diagnoses from direct semistructured interviews of 190 patients by trained clinicians blind to EHR diagnosis. RESULTS: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR-classified control subject received a diagnosis of bipolar disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. CONCLUSIONS: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.
Kohane IS. Ten things we have to do to achieve precision medicine. Science [Internet]. 2015;349 :37-8. Publisher's Version kohane_pm10_science_2015.pdf
2014
Weber GM, Mandl KD, Kohane IS. Finding the Missing Link for Big Biomedical Data. JAMAJAMA. 2014.
Wang F, Remke M, Bhat K, Wong ET, Zhou S, Ramaswamy V, Dubuc A, Fonkem E, Salem S, Zhang H, et al. A microRNA-1280/JAG2 network comprises a novel biological target in high-risk medulloblastoma. Oncotarget. 2014.Abstract
Over-expression of PDGF receptors (PDGFRs) has been previously implicated in high-risk medulloblastoma (MB) pathogenesis. However, the exact biological functions of PDGFRalpha and PDGFRbeta signaling in MB biology remain poorly understood. Here, we report the subgroup specific expression of PDGFRalpha and PDGFRbeta and their associated biological pathways in MB tumors. c-MYC, a downstream target of PDGFRbeta but not PDGFRalpha, is involved in PDGFRbeta signaling associated with cell proliferation, cell death, and invasion. Concurrent inhibition of PDGFRbeta and c-MYC blocks MB cell proliferation and migration synergistically. Integrated analysis of miRNA and miRNA targets regulated by both PDGFRbeta and c-MYC reveals that increased expression of JAG2, a target of miR-1280, is associated with high metastatic dissemination at diagnosis and a poor outcome in MB patients. Our study may resolve the controversy on the role of PDGFRs in MB and unveils JAG2 as a key downstream effector of a PDGFRbeta-driven signaling cascade and a potential therapeutic target.

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