microRNAs (miRNAs) have been reported to play an essential role in the regulation of brown adipocyte adipogenesis. In the present study, we investigated the role of the miR-106b-93 cluster in the differentiation of brown adipocytes. We found that knockdown of miR-106b and miR-93 significantly induced the expression of brown fat-specific genes and promoted the accumulation of lipid-droplet in differentiating brown adipocytes. In addition, ectopic expression of miR-106b and miR-93 suppressed the mRNA level of Ucp1, a selective hallmark of brown adipocytes. Furthermore, the expression levels of miR-106b and miR-93 are higher in brown adipose tissues of high fat diet-induced obese mice compared to control mice. Taken together, our results identify miR-106b and miR-93 as negative regulators of brown adipocyte differentiation and the miR-106b-93 cluster may play an important role in regulating energy homeostasis.

The British Journal of Haematology publishes original research papers in clinical, laboratory and experimental haematology. The Journal also features annotations, reviews, short reports, images in haematology and Letters to the Editor.

Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.

Assembly factors are necessary for the formation of mitochondrial supercomplexes (SCs) and in making cellular respiration more efficient. In a recent study, Balsa et al. (Mol. Cell, 2019) report that nutrient-induced endoplasmic reticulum (ER) stress engages PERK–eIF2α-mediated transcription of the SCs assembly factor SCAF1, events that coordinate ER stress and SCs formation to improve bioenergetics.

Abstract Hypomethylating agents (HMA) and lenalidomide (LEN) are approved and used in the treatment of patients (pts) with MDS, though these drugs fail in most pts. No method exists to predict drug response beyond associating single actionable mutations with a single drug's response. We hypothesized that MDS pts can be clustered by similarities in genomic/molecular profiles, & that each cluster may be assigned combos of FDA-approved drugs to target their unique biomarker profile. Bone marrow cells from 88 MDS pts & the MDS-L cell line were analyzed by cytogenetics & for mutations in 14 myeloid genes using NGS. 31 pts had sufficient data for analysis. 20 profiles had similar aberrations & were grouped. Genomic data from pts and MDS-L were entered into a computational biology modeling (CBM) software, which generates a disease-specific protein network map using PubMed to create digital models and identify characteristic biomarkers unique to each pt. An algorithm was created to cluster the models based on overlapping disease-specific biomarkers. Digital drug simulations (DDS) were conducted both on MDS-L & pt simulation models by quantitatively measuring drug effect on a cell growth score (CGS), a composite of cell proliferation, viability & apoptosis. DDS identified drugs by assessing their impact on disease-specific biomarkers and calculated CGSs. Predictions were validated using MTT. 14/31 MDS pt profiles, including the MDS-L cell line, clustered into 4 groups based on biomarker characteristics.MDS-L cells harbor NRAS (G12A) mutation,-7,-12,+1,+8,+19,+20 and +21. Genes associated with increased copy number (CN) include AURKA, IGFR, PAR5, MTOR, IL6, JAK3, MDM4, MYC, MCL1, COX2, PDE4A, and RCE1; genes associated with decreased CN include DUSP1, RASA1, NR3C1, IRF1, ETV6, and SHH. CBM identified active biomarkers in MDS-L cells (90RSK, MAPK7, AKT and BTK), validated by western blot. DDS predicted nelfinavir+celecoxib to be effective in MDS-L. MDS-L cells were treated with nelfinavir, celecoxib, and nelfinavir+celecoxib with increasing doses. Nelfinavir & celecoxib reduced MDS-L viability in a dose-dependent manner, while nelfinavir+celecoxib showed additive reduction of MDS-L viability. DDS was performed on each pt to predict response to HMA and LEN. Two of the clusters (n=2/cluster) were predicted to be non-responders to any SOC therapy. A third cluster (n=3) was predicted to respond to HMA, but not LEN, and the fourth cluster (n=6) showed varying or no response to either HMA/LEN. These results use a novel concept of using genomics & CBM to cluster profiles with overlapping disease-relevant biomarkers & similar drug response predictions. CBM can identify pt populations who may benefit from certain therapeutic regimens, improving response rates, & give insights into the mechanisms by which each drug impacts the MDS-specific biomarkers Citation Format: Leylah M. Drusbosky, Kimberly E. Hawkins, Amy Meacham, Elizabeth Wise, Neeraj Kumar Singh, Chandan Kumar, Sumanth M. Vasista, Rakhi P. Suseela, Taher Abbasi, Shireen Vali, Kaoru Tohyama, Maher Albitar, Peter P. Sayeski, Christopher R. Cogle. Genomics-driven clustering of disease-related biomarkers identifies therapeutic options in myelodysplastic syndromes (MDS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4285.

Abstract Droplet digital PCR (ddPCR) is a highly sensitive and rapid method for detecting mutant allele frequency (MAF). In preliminary work, our lower limit of detection for common myeloid gene mutations was 0.001% in peripheral blood and bone marrow compared to 0.1% with flow cytometry and 0.01% with real-time quantitative PCR, and turnaround time is 1 day. Furthermore, we detected leukemic mutant alleles in peripheral blood (PB), introducing the possibility of sparing painful bone marrow biopsy procedures to determine treatment response. Thus, we hypothesized that ddPCR is a feasible and accurate method for monitoring leukemic disease burden in PB for the prospective care of patients (pts) with AML. Eighteen patients (pts) with de novo, relapse/refractory, and secondary AML were recruited to an IRB-approved study (NCT02435550) and bone marrow (BM), peripheral blood (PB), and saliva were collected at standard clinical visits. Gene mutations were identified by whole-exome sequencing (WES) of BM specimens at study entry. For ddPCR interrogation, genomic DNA was isolated (Qiagen), and select primers and probes (Bio-Rad/IDT) were developed based on variants identified in WES data. Case-specific primers and probes were validated on archived specimens obtained at study entry. 12/18 pt mutanomes met criteria for primer/probe design. 8 pts are in the primer/probe design and validation stage and 4 have completed validation and serial analyses. WES identified, and ddPCR confirmed, at least 1 mutation per patient at the study entry timepoint. The mutations included NRAS G13R, NRAS G12A, CSF3R T618I, and IDH2 R172K. In 2 cases, we observed a reduction in both PB and saliva MAF that were consistent with the reduction in both BM and PB blasts after treatment, resulting in complete remissions. Although PB blasts were reduced in a third pt receiving ruxolitinib, the persistence of their CSF3R MAF in PB indicated a resistant AML clone. WES revealed the presence of NRAS G13R variant in a secondary AML pt; however, WES did not detect this NRAS G13R variant in a cryopreserved BM specimen obtained at the pts MDS diagnosis. Interestingly, ddPCR was able to detect NRAS G13R variant at 0.1% MAF in a PB sample obtained at MDS diagnosis, demonstrating the ultrasensitive detection of rare variants within a sample, and highlighting the subclonal evolution of this pt's malignancy. Rapid detection of myeloid-related somatic mutations in a variety of tissue sources (i.e., saliva, PB) will allow for noninvasive monitoring of AML tumor burden. ddPCR may be used to observe molecular response to treatment and to detect molecular residual disease and relapse prior to clinically indicated BM biopsies. Citation Format: Kimberly E. Hawkins, Cesia Salan, Madeleine Turcotte, Lauren T. Vaughn, Mei Zhang, Yanping Zhang, Barry Sawicki, Glenda G. Anderson, Nosha Farhadfar, Hemant S. Murthy, Biljana N. Horn, Helen L. Leather, Paul Castillo, Maxim Norkin, John W. Hiemenz, Randy A. Brown, William Slayton, Jack W. Hsu, John R. Wingard, Christopher R. Cogle, Leylah M. Drusbosky. Droplet digital PCR is a sensitive method for detecting refractory acute myeloid leukemia (AML) clones in peripheral blood and saliva [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3253.

As part of the neurovascular unit, the blood-brain barrier (BBB) is a unique, dynamic regulatory boundary that limits and regulates the exchange of molecules, ions, and cells between the blood and the central nervous system. Disruption of the BBB plays an important role in the development of neurological dysfunction in ischemic stroke. Blood-borne substances and cells have restricted access to the brain due to the presence of tight junctions between the endothelial cells of the BBB. Following stroke, there is loss of BBB tight junction integrity, leading to increased paracellular permeability, which results in vasogenic edema, hemorrhagic transformation, and increased mortality. Thus, understanding principal mediators and molecular mechanisms involved in BBB disruption is critical for the development of novel therapeutics to treat ischemic stroke. This review discusses the current knowledge of how neuroinflammation contributes to BBB damage in ischemic stroke. Specifically, we provide an updated overview of the role of cytokines, chemokines, oxidative and nitrosative stress, adhesion molecules, matrix metalloproteinases, and vascular endothelial growth factor as well as the role of different cell types in the regulation of BBB permeability in ischemic stroke.

Care coordination has been shown to have a positive effect on the management of chronic disease. Specific to the management of leukemia, coordination may occur between primary care physician, medical and radiation oncologists, surgeons, cardiologists, and genetics specialists. Experiencing gaps in communication and care coordination, many health consumers seek instrumental support in their social circles, including online forums and networks. The goal of this theory-guided study was to provide an in-depth assessment of how individuals use online forums to deliberate about their goals and plans for leukemia care coordination. Guided by the planning theory of communication, the data were collected from the American Cancer Society Cancer Survivors Network and included 125 original posts and 1,248 responses. Thematic analysis and axial coding were applied to analyze the data. Goal-related themes included overcoming the diffusion of care coordination and achieving health management cohesion. Planning themes included social health management, communication self-efficacy, and role deliberation. Online patient forums provide an interactive platform for patients and caregivers to engage in active conversations, which in turn can serve as identifiers of care coordination needs. Communication with those who share similar experiences allows cancer patients and survivors to accumulate functional health literacy, gain communication self-efficacy, and articulate a care coordination role acceptable to them.

ETP-ALL is a high-risk subtype of T-ALL with poor prognosis and high risk of relapse in patients treated with standard of care (SOC). ETP-ALL patients have more complex cytogenetic profiles and a unique immunophenotype compared to other T-ALL cases. ETP-ALL is characterized by the expression of myeloid and hematopoietic stem cell (HSC) surface proteins, as well as gene expression profiles similar to myeloid progenitors and HSCs, including frequent mutations in myeloid oncogenes. Targeted therapies for ETP-ALL are limited because identified gene mutations are not actionable by currently available drugs. Recently we developed a predictive simulation modeling method that projects cancer patient-specific protein network maps based on the patient's unique cancer genomics (PMID 27855285). Here, we present a computational biology approach to analyze the genomic profile of ETP-ALL and identify novel treatment strategies using available FDA approved drugs. In this study, we used the LOUCY cell line as a model of ETP-ALL due to its high expression of characteristic ETP-ALL markers HHEX, LYL1, MEF2C and ZEB2.

Although the majority of MDS patients fail to achieve clinical improvement to approved therapies, some patients benefit from treatment. Predicting patient response prior to therapy would improve treatment effectiveness, avoid treatment-related adverse events and reduce healthcare costs. Three separate cohorts of MDS patients were used to simulate drug response to lenalidomide alone, hypomethylating agent (HMA) alone, or HMA plus lenalidomide. Utilizing a computational biology program, genomic abnormalities in each patient were used to create an intracellular pathway map that was then used to screen for drug response. In the lenalidomide treated cohort, computer modeling correctly matched clinical responses in 37/46 patients (80%). In the second cohort, 15 HMA patients were modeled and correctly matched to responses in 12 (80%). In the third cohort, computer modeling correctly matched responses in 10/10 patients (100%). This computational biology network approach identified GGH overexpression as a potential resistance factor to HMA treatment and paradoxical activation of beta-catenin (through Csnk1a1 inhibition) as a resistance factor to lenalidomide treatment. We demonstrate that a computational technology is able to map the complexity of the MDS mutanome to simulate and predict drug response. This tool can improve understanding of MDS biology and mechanisms of drug sensitivity and resistance.

P-glycoprotein (P-gp) is known to transport a diverse array of xenobiotics, including therapeutic drugs. A member of the ATP-binding cassette (ABC) transporter family, P-gp is a protein encoded by the gene Mdr1 in humans and Abcb1 in rodents (represented by 2 isoforms Abcb1a and Abcb1b). Lining the luminal and abluminal membrane of brain capillary endothelial cells, P-gp is a promiscuous efflux pump extruding a variety of exogenous toxins and drugs. In this study, we measured dynamic changes in Abcb1a and Abcb1b transcripts and P-gp protein in the brain, liver, and kidney after experimental stroke. P-glycoprotein has been shown to increase in brain endothelial cells following hypoxia in vitro or after exposure to proinflammatory cytokines. Using a rat model of ischemic stroke, we hypothesized that P-gp expression will be increased in the brain, liver, and kidney in response to neuroinflammation following ischemic stroke. Adult Sprague Dawley rats underwent middle cerebral artery occlusion (MCAO) for 90 minutes and were killed at 4, 14, 24, and 48 hours postreperfusion onset to determine the time course of P-gp expression. To mimic ischemia occurring at the blood-brain barrier, rat brain endothelial (RBE4) cells were subjected to hypoxia and low glucose (HLG) for 16 hours. Immunoblotting analyses showed P-gp increases in brain and liver following 90-minute MCAO, as well as in cultured RBE4 cells after 16-hour HLG treatment, but fluctuated in the kidney depending on the time point. The relative roles of each isoform in the protein expression were analyzed with quantitative reverse transcriptase polymerase chain reaction. Ischemic stroke leads to significant increases in P-gp levels not only in the brain but also in the liver. The increase in P-gp could dramatically reduce the bioavailability and efficacy of neuroprotective drugs. Therefore, P-gp represents a big hurdle to drug delivery to the ischemic brain.

BACKGROUND:Resolution of inflammation is an emerging new strategy to reduce damage following ischemic stroke. Lipoxin A4 (LXA 4) is an anti-inflammatory, pro-resolution lipid mediator that reduces neuroinflammation in stroke. Since LXA 4 is rapidly inactivated, potent analogs have been synthesized, including BML-111. We hypothesized that post-ischemic, intravenous treatment with BML-111 for 1 week would provide neuroprotection and reduce neurobehavioral deficits at 4 weeks after ischemic stroke in rats. Additionally, we investigated the potential protective mechanisms of BML-111 on the post-stroke molecular and cellular profile. METHODS:A total of 133 male Sprague-Dawley rats were subjected to 90 min of transient middle cerebral artery occlusion (MCAO) and BML-111 administration was started at the time of reperfusion. Two methods of week-long BML-111 intravenous administration were tested: continuous infusion via ALZET ® osmotic pumps (1.25 and 3.75 μg μl-1 hr-1), or freshly prepared daily single injections (0.3, 1, and 3 mg/kg). We report for the first time on the stability of BML-111 and characterized an optimal dose and a dosing schedule for the administration of BML-111. RESULTS:One week of BML-111 intravenous injections did not reduce infarct size or improve behavioral deficits 4 weeks after ischemic stroke. However, post-ischemic treatment with BML-111 did elicit early protective effects as demonstrated by a significant reduction in infarct volume and improved sensorimotor function at 1 week after stroke. This protection was associated with reduced pro-inflammatory cytokine and chemokine levels, decreased M1 CD40+ macrophages, and increased alternatively activated, anti-inflammatory M2 microglia/macrophage cell populations in the post-ischemic brain. CONCLUSION:These data suggest that targeting the endogenous LXA 4 pathway could be a promising therapeutic strategy for the treatment of ischemic stroke. More work is necessary to determine whether a different dosing regimen or more stable LXA 4 analogs could confer long-term protection.

Data from a phase II clinical trial of venetoclax in AML recently supported its FDA breakthrough therapy designation for use in combination with hypomethylating agents in untreated AML patients ineligible for standard induction chemotherapy. Venetoclax is currently FDA approved for use in chronic lymphoid leukemia (CLL) patients with a 17(p) deletion who have been treated with at least 1 prior therapy. Del17(p) leads to loss of the TP53 gene, encoding the key tumor suppressor p53. Loss of p53 results in BCL-2 overexpression, providing a target for venetoclax's mechanism of action. Approval of this BCL-2 inhibitor in CLL raises the question of mechanism of action in AML and patient selection for treatment. Unfortunately, no biomarkers or methods exist to predict venetoclax response in AML, making treatment selection challenging.

In this chapter we consider human disorders of memory and their possible underlying genetic and molecular mechanisms. The genetic and molecular basis of Neurofibromatosis type I and other RASopathies, Angelman syndrome, Fragile X syndrome, Williams–Beuren syndrome, Down syndrome, Pitt-Hopkins syndrome, and Rett syndrome will be discussed. An overview of current research into suitable models of disease and treatment will provide insight into the value of basic scientific research in understanding human memory disorders and intellectual disabilities.

Hypomethylating agents (HMAs) (azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used to treat patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Despite their widespread use, HMAs fail in the majority of these patients, and len fails in 75% of non-del(5q) MDS. Currently, no method exists to predict disease response, thus the management of MDS and AML patients is challenging. Methods: Patients with AML or MDS were recruited to a clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of drug response to actual clinical response. Genomic profiling was conducted by cytogenetics, whole exome sequencing, and array CGH. Genomic results were inputted into a computational software (Cellworks), which generates disease-specific protein network maps using PubMed and other resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score (proliferation + viability + apoptosis). Each patient-specific protein network was screened for the extent by which aza, dec or len reduced disease growth in a dose-respondent manner. Treatment was physician’s choice of SOC. Clinical outcomes were prospectively recorded. IWG criteria were used to define response. Western blot assays were performed to validate the predicted protein network perturbations. Fisher’s exact test was used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: 88 patients have had all molecular tests and computational modeling performed. Lab validation of computer-predicted, activated protein networks in 19 samples from 13 different patients showed correct prediction of 5 activated networks (Akt2, Akt3, PIK3CA, p38, Erk1/2) in 17 samples, with 89% accuracy. At the time of this report, 26/88 patients were eligible for efficacy evaluation. 8/26 patients showed clinical response to SOC therapy, 18/26 did not. 24/26 outcome predictions were correctly matched to their clinical outcomes, and 2/20 were incorrectly matched, resulting in 92% prediction accuracy, 80% PPV, 100% NPV, 100% sensitivity, and 89% specificity. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration (p=1.664e-05). New genomic signature rules were discovered to correlate with clinical response after aza, dec or len. Summary: A computational method that models multiple genomic abnormalities simultaneously showed high predictive value of protein network aberrations and clinical outcomes after SOC treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials Citation Format: Leylah Drusbosky, Kimberly E. Hawkins, Shireen Vali, Taher Abbasi, Ansu Kumar, Neeraj Kumar Singh, Kabya Basu, Chandan Kumar, Amjad Husain, Caitlin Tucker, Randy A. Brown, Maxim Norkin, John Hiemenz, Jack Hsu, John Wingard, Christopher R. Cogle. iCare 1: A prospective clinical trial to predict treatment response based on mutanome-informed computational biology in patients with AML and MDS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT085. doi:10.1158/1538-7445.AM2017-CT085

Epigenetic mechanisms that help regulate gene activity in the central nervous system have historically been thought to be involved exclusively in developmental processes or in disease states. However, recent work argues that these mechanisms, particularly posttranslational modifications of histones and covalent modification of DNA, remain labile through out the life span and are altered by experiences. Indeed, exciting new data indicate that epigenetic regulation of genes is necessary for experience-induced changes in adult brain function and behavior. In this chapter, we focus on this theme and review the data implicating epigenetic mechanisms in neural plasticity and memory formation. As this is still considered a relatively new transcriptional regulatory mechanism in the context of memory formation, we also discuss some of the caveats and limitations of the data.

Refractory disease remains a challenge in treating patients (pts) with MDS and AML. Despite their widespread use, cytotoxic agents (7+3, HiDAC) and hypomethylating agents (HMAs) fail in the majority of pts, and lenalidomide (LEN) fails in 75% of non-del(5q) MDS. 90% of MDS and AML pts harbor at least one somatic mutation, further contributing to disease complexity. Currently, no comprehensive method exists to predict disease response based on the entire mutational burden detected in malignant samples. Predicting response to treatment would improve effectiveness, limit treatment-related adverse events, and reduce health care costs. Hence, there is need to predict pt response based on disease biology. Aim: Determine the biological and clinical predictive values of a genomics-informed computational biology method (CBM) in pts who are treated with standard of care (SOC) therapy. Methods: Pts with AML or MDS were recruited in a prospective clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of treatment response to actual clinical response. Genomic profiling was conducted by cytogenetics, whole exome sequencing (WES), and copy number variation (CNV) analysis. These genomic results were entered into a CBM software (Cellworks Group), which generates disease-specific protein network maps using PubMed and other online resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each pt-specific protein network map was digitally screened for the extent to which each pt9s therapy reduced simulated disease growth in a dose-respondent manner. Treatment was physician9s choice of SOC. Before initiating treatment, treating physicians were masked to the results of WES and CBM predictions. Clinical outcomes were prospectively recorded by the physicians. To be eligible for efficacy assessment, MDS pts must have had at least 4 cycles of HMA treatment or 2 cycles of LEN treatment. For AML, CR+PR was used to define response (IWG 2003). For MDS, CR+PR+HI was used to define response (IWG 2006). AML pt response was determined at time of recovery bone marrow biopsy, post induction therapy and/or consolidation therapy. Western blot assays were performed to validate the predicted protein network perturbations. Comparisons of CBM versus actual responses were performed using 2x2 tables. Fisher9s exact tests were used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: Between June 2015 and July 2017, 120 pts were recruited, and 81 have had all genetic tests performed. At the time of this report, 42 pts were eligible for clinical response evaluation. 25/42 (60%) had AML, 15/42 had MDS (36%), and 2 pts had myelofibrosis. The median age was 65 (range 47-90). 24/42 (57%) were treatment-naive and 18/42 (43%) were treatment-refractory. Laboratory validation study of computer-predicted, activated protein networks in 19 samples from 11 different pts showed correct prediction of 4 activated networks (Akt2, Akt3, PIK3CA, Erk1/2) in 17 samples, exhibiting 89% accuracy. At the time of this report, 42/81 pts received 52 treatments and were eligible for efficacy evaluation. 28/52 treatments resulted in clinical response to SOC therapy, while 24/52 did not achieve clinical response. 48 drug outcome predictions (92%) were correctly matched to their actual clinical outcomes, and 4/52 were incorrectly matched, resulting in 93% PPV, 92% NPV, 93% sensitivity, and 92% specificity (Table 1). Of note, CBM performed accurately in both treatment-naive and relapsed/refractory pts. The accuracy of the CBM was significantly greater than empiric drug administration (p=6.034 E-08). Additionally, new genomic signature rules were discovered to correlate with clinical response after HMA or len. Conclusions: CBM that models multiple genomic abnormalities simultaneously showed high predictive value of protein network perturbations and clinical outcomes after SOC treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials, and will be used in upcoming precision medicine trials. Disclosures Singh: Cellworks Research India Pvt. Ltd: Employment. Radhakrishnan: Cellworks: Employment. Ullal: Cellworks Research India: Employment. Talawdekar: Cellworks: Employment. Sikora: Cellworks: Employment. Nair: Cellworks: Employment. Bhowmick: Cellworks: Employment. Abbasi: Cellworks Group Inc.: Employment. Vali: Cellworks Group Inc.: Employment. Norkin: Celgene: Honoraria, Research Funding. Cogle: Celgene: Other: Membership on Steering Committee for Connect MDS/AML Registry.

Background: Although virtually all patients (pts) with AML or MDS harbor at least one somatic mutation, a minority of pts possess genetic mutations considered directly targetable by a drug. Moreover, in pts with multiple gene mutations, single-gene/single-drug matching often produces conflicting drug recommendations. This lack of clinically relevant mutation-targeted therapy represents an unmet need in treating AML and MDS, and is a major limitation in personalized medicine. We hypothesized that genomic mutations from pts with AML or MDS could be used to generate pt-specific protein network maps for use in digital drug screening (DDS) even in cases when the gene mutations per se are not directly actionable. This would dramatically increase the percent of pts with actionable findings. Our primary goal was to establish a genomics and computational biology workflow that identified disease-relevant treatment options for every pt.

Sleep deprivation is well established to cause diminution of cognitive function, including disruption of both minute-to-minute working memory and decrements in the stabilization of long-term memories. Moreover, "replay" during sleep of episodes and sequences of events that were experienced during wakefulness has been implicated in consolidation of long-term memories. However, the molecular mechanisms underlying the role of sleep in memory function are just starting to be defined. In this issue of Science Signaling, Tudor et al identify one molecular component underlying the effects of sleep on memory function: dynamic experience-dependent regulation of protein synthesis in the hippocampus.

Adropin is a peptide encoded by the energy homeostasis associated gene (Enho) and plays a critical role in the regulation of lipid metabolism, insulin sensitivity, and endothelial function. Little is known of the effects of adropin in the brain and whether this peptide modulates ischemia-induced blood-brain barrier (BBB) injury. Here, we used an in vitro BBB model of rat brain microvascular endothelial cells (RBE4) and hypothesized that adropin would reduce endothelial permeability during ischemic conditions. To mimic ischemic conditions in vitro, RBE4 cell monolayers were subjected to 16 h hypoxia/low glucose (HLG). This resulted in a significant increase in paracellular permeability to FITC-labeled dextran (40 kDa), a dramatic upregulation of vascular endothelial growth factor (VEGF), and the loss of junction proteins occludin and VE-cadherin. Notably, HLG also significantly decreased Enho expression and adropin levels. Treatment of RBE4 cells with synthetic adropin (1, 10 and 100 ng/ml) concentration-dependently reduced endothelial permeability after HLG, but this was not mediated through protection to junction proteins or through reduced levels of VEGF. We found that HLG dramatically increased myosin light chain 2 (MLC2) phosphorylation in RBE4 cells, which was significantly reduced by adropin treatment. We also found that HLG significantly increased Rho-associated kinase (ROCK) activity, a critical upstream effector of MLC2 phosphorylation, and that adropin treatment attenuated that effect. These data indicate that treatment with adropin reduces endothelial cell permeability after HLG insult by inhibition of the ROCK-MLC2 signaling pathway. These promising findings suggest that adropin protects against endothelial barrier dysfunction during ischemic conditions.