Waldenström's macroglobulinemia is an incurable, IgM-secreting lymphoplasmacytic lymphoma (LPL). The underlying mutation in this disorder has not been delineated.
We performed whole-genome sequencing ...of bone marrow LPL cells in 30 patients with Waldenström's macroglobulinemia, with paired normal-tissue and tumor-tissue sequencing in 10 patients. Sanger sequencing was used to validate the findings in samples from an expanded cohort of patients with LPL, those with other B-cell disorders that have some of the same features as LPL, and healthy donors.
Among the patients with Waldenström's macroglobulinemia, a somatic variant (T→C) in LPL cells was identified at position 38182641 at 3p22.2 in the samples from all 10 patients with paired tissue samples and in 17 of 20 samples from patients with unpaired samples. This variant predicted an amino acid change (L265P) in MYD88, a mutation that triggers IRAK-mediated NF-κB signaling. Sanger sequencing identified MYD88 L265P in tumor samples from 49 of 54 patients with Waldenström's macroglobulinemia and in 3 of 3 patients with non-IgM-secreting LPL (91% of all patients with LPL). MYD88 L265P was absent in paired normal tissue samples from patients with Waldenström's macroglobulinemia or non-IgM LPL and in B cells from healthy donors and was absent or rarely expressed in samples from patients with multiple myeloma, marginal-zone lymphoma, or IgM monoclonal gammopathy of unknown significance. Inhibition of MYD88 signaling reduced IκBα and NF-κB p65 phosphorylation, as well as NF-κB nuclear staining, in Waldenström's macroglobulinemia cells expressing MYD88 L265P. Somatic variants in ARID1A in 5 of 30 patients (17%), leading to a premature stop or frameshift, were also identified and were associated with an increased disease burden. In addition, 2 of 3 patients with Waldenström's macroglobulinemia who had wild-type MYD88 had somatic variants in MLL2.
MYD88 L265P is a commonly recurring mutation in patients with Waldenström's macroglobulinemia that can be useful in differentiating Waldenström's macroglobulinemia and non-IgM LPL from B-cell disorders that have some of the same features. (Funded by the Peter and Helen Bing Foundation and others.).
Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data ...using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.
The combination of data, computing power, and advanced analytics is positioning data science as a critical core discipline in pharmaceutical research, alongside the more traditional disciplines of ...biology, chemistry, and medicine. ...machine learning engineers and specialized data scientists with specific skillsets (e.g., deep learning, image processing, or body sensors analysis) have joined the ranks of growing data science teams in pharmaceutical companies. Inclusion of data science leaders in decision-making bodies connects data scientists to critical business questions, raises organizational awareness of computational approaches and data management, and further connects disease-focused departments with discovery and clinical platforms. Over time, important information is lost due to organizational changes and employee turnover, either because the data are not well documented or because they are stored in nonstandard or nonmachine-readable formats. ...it is crucial to have in place FAIR play processes from the point of data generation, including clear data and metadata management strategies.
In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, ...fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.
•Novartis formed a program to make access and reuse of (R&D) data routine.•Many hurdles frequently prevent streamlined and scaled use of clinical trial data.•We owe patients to protect their data ...while maximize its use to advance medicine.•A blueprint for companies to accelerate their progress in a patient-first manner.•A majority of internal requests for clinical data, can be automatically approved.
Enabling broad access and usage of clinical trial data within biopharmaceutical companies has historically been impeded by technical, cultural, and policy hurdles. Novartis has attempted to address this comprehensively through a program called data42; here, we explore how a diverse set of enterprise-wide stakeholders formulated a risk-based data access approach to streamline access to anonymized clinical trial data and vastly improved its use by authorized research and development (R&D) associates within the company. The result is that most Novartis clinical trial data requests, from internal associates, can now be automatically approved. The process of developing this framework and its impact on Novartis and the broader industry are explored and discussed.
Preβ-1 high-density lipoprotein (HDL) plays a key role in reverse cholesterol transport by promoting cholesterol efflux. Our aims were (1) to test previous associations between preβ-1 HDL and ...coronary heart disease (CHD) and (2) to investigate whether preβ-1 HDL levels also are associated with risk of myocardial infarction (MI). Plasma preβ-1 HDL was measured by an ultrafiltration–isotope dilution technique in 1,255 subjects recruited from the University of California–San Francisco Lipid and Cardiovascular Clinics and collaborating cardiologists. Preβ-1 HDL was significantly and positively associated with CHD and MI even after adjustment for established risk factors. Inclusion of preβ-1 HDL in a multivariable model for CHD led to a modest improvement in reclassification of subjects (net reclassification index 0.15, p = 0.01; integrated discrimination improvement 0.003, p = 0.2). In contrast, incorporation of preβ-1 HDL into a risk model of MI alone significantly improved reclassification of subjects (net reclassification index 0.21, p = 0.008; integrated discrimination improvement 0.01, p = 0.02), suggesting that preβ-1 HDL has more discriminatory power for MI than for CHD in our study population. In conclusion, these results confirm previous associations between preβ-1 HDL and CHD in a large well-characterized clinical cohort. Also, this is the first study in which preβ-1 HDL was identified as a novel and independent predictor of MI above and beyond traditional CHD risk factors.
The ‘expanded’ HD CAG repeat that causes Huntington's disease (HD) encodes a polyglutamine tract in huntingtin, which first targets the death of medium-sized spiny striatal neurons. Mitochondrial ...energetics, related to N-methyl-d-aspartate (NMDA) Ca2+-signaling, has long been implicated in this neuronal specificity, implying an integral role for huntingtin in mitochondrial energy metabolism. As a genetic test of this hypothesis, we have looked for a relationship between the length of the HD CAG repeat, expressed in endogenous huntingtin, and mitochondrial ATP production. In STHdhQ111 knock-in striatal cells, a juvenile onset HD CAG repeat was associated with low mitochondrial ATP and decreased mitochondrial ADP-uptake. This metabolic inhibition was associated with enhanced Ca2+-influx through NMDA receptors, which when blocked resulted in increased cellular ATP/ADP. We then evaluated ATP/ADP in 40 human lymphoblastoid cell lines, bearing non-HD CAG lengths (9–34 units) or HD-causing alleles (35–70 units). This analysis revealed an inverse association with the longer of the two allelic HD CAG repeats in both the non-HD and HD ranges. Thus, the polyglutamine tract in huntingtin appears to regulate mitochondrial ADP-phosphorylation in a Ca2+-dependent process that fulfills the genetic criteria for the HD trigger of pathogenesis, and it thereby determines a fundamental biological parameter—cellular energy status, which may contribute to the exquisite vulnerability of striatal neurons in HD. Moreover, the evidence that this polymorphism can determine energy status in the non-HD range suggests that it should be tested as a potential physiological modifier in both health and disease.
Pulmonary function measures obtained by spirometry are used to diagnose chronic obstructive pulmonary disease (COPD) and are highly heritable. We conducted genome-wide association (GWA) analyses ...(Affymetrix 100K SNP GeneChip) for measures of lung function in the Framingham Heart Study.
Ten spirometry phenotypes including percent of predicted measures, mean spirometry measures over two examinations, and rates of change based on forced expiratory volume in one second (FEV1), forced vital capacity (FVC), forced expiratory flow from the 25th to 75th percentile (FEF25-75), the FEV1/FVC ratio, and the FEF25-75/FVC ratio were examined. Percent predicted phenotypes were created using each participant's latest exam with spirometry. Predicted lung function was estimated using models defined in the set of healthy never-smokers, and standardized residuals of percent predicted measures were created adjusting for smoking status, pack-years, and body mass index (BMI). All modeling was performed stratified by sex and cohort. Mean spirometry phenotypes were created using data from two examinations and adjusting for age, BMI, height, smoking and pack-years. Change in pulmonary function over time was studied using two to four examinations with spirometry to calculate slopes, which were then adjusted for age, height, smoking and pack-years.
Analyses were restricted to 70,987 autosomal SNPs with minor allele frequency > or = 10%, genotype call rate > or = 80%, and Hardy-Weinberg equilibrium p-value > or = 0.001. A SNP in the interleukin 6 receptor (IL6R) on chromosome 1 was among the best results for percent predicted FEF25-75. A non-synonymous coding SNP in glutathione S-transferase omega 2 (GSTO2) on chromosome 10 had top-ranked results studying the mean FEV1 and FVC measurements from two examinations. SNPs nearby the SOD3 and vitamin D binding protein genes, candidate genes for COPD, exhibited association to percent predicted phenotypes.
GSTO2 and IL6R are credible candidate genes for association to pulmonary function identified by GWA. These and other observed associations warrant replication studies. This resource of GWA results for pulmonary function measures is publicly available at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.
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