scholarly journals NGS-Based MRD Quantitation: An Alternative to qPCR Validated on a Large Consecutive Cohort of Children with ALL

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1314-1314
Author(s):  
Michael Svaton ◽  
Aneta Skotnicova ◽  
Leona Reznickova ◽  
Andrea Rennerova ◽  
Tatana Valova ◽  
...  
Keyword(s):  
False Positive ◽  
Research Funding ◽  
Service Level Agreement ◽  
Service Level ◽  
Advisory Board ◽  
Patient Specific ◽  
Gene Rearrangements ◽  
Sequencing Data ◽  
Treatment Protocols ◽  
Routine Identification

Abstract Together with multicolor flow cytometry, quantitation of clonal immunoglobulin (IG) and T-cell receptor (TR) gene rearrangements represents the current standard for the detection of minimal / measurable residual disease (MRD) in treatment protocols for pediatric acute lymphoblastic leukemia (ALL) patients. Despite the adoption of next generation sequencing (NGS) in the routine identification of clonal IG/TR gene rearrangements as markers for MRD detection, real-time quantitative (q)PCR is still the standard for MRD quantitation in follow-up samples. So far, no large-scale direct comparison of qPCR- and NGS-based MRD quantitation has been performed. We compared qPCR- and NGS-MRD evaluation in a cohort of children with B-cell precursor (BCP) ALL treated on the AIEOP-BFM ALL 2009 protocol and assessed the feasibility and relevance of this method for the stratification at day 33 (EOI). In total, 459 patients were diagnosed with BCP-ALL from 2010 to 2018, and 437 of them were included in our study based on the availability of residual DNA material isolated from day 33 bone marrow aspirates and having at least one IG/TR MRD marker detectable by standard qPCR with protocol-required sensitivity of 10 -4. Sequencing libraries were prepared according to the EuroClonality-NGS group SOP (Brüggemann et al, Leukemia 2019) with the total DNA input normalized to the equivalent of 150,000 nucleated cells to reach MRD sensitivity of 10 -5 and sequenced on Illumina NovaSeq and MiSeq instruments. In total of 780 IG/TR markers evaluated by both NGS and qPCR. Sequencing data were analyzed using the ARResT/Interrogate (Bystry et at, Bioinformatics 2017) pipeline and a custom bioinformatic analysis process and the NGS-MRD results were normalized to the EuroClonality-NGS central in-tube quality/quantification control (cIT-QC; Knecht et al, Leukemia 2019). From the total 780 IG/TR MRD markers evaluated by both methods, 629 (80.6%) were concordant with 242 markers being MRD positive and 387 negative. From 82 markers that were only positive by qPCR and not by NGS, 76 were positive below the quantitative range (positive non-quantifiable). Specificity analysis was performed for each marker by searching for the junction sequence across the dataset of all patients' NGS results. Based on these results, 22 out of 82 markers positive only by qPCR were classified as potentially unspecific (false positive) and similarly 32 unspecific markers were identified among the 69 positive only by NGS. This was also supported by unspecific amplification of the polyclonal control in 27 out of these 32 corresponding qPCR systems, in some cases leading to qPCR negative classification determined by the EuroMRD guidelines. Overall stratification of patients based only on day 33 MRD by qPCR or NGS was concordant in 76% of patients by both methods, while in 19% of patients, NGS-MRD quantitation led to the assignment to a lower-risk group, mainly due to the elimination of false-positive results. Furthermore, analysis of all positive markers across all patients' NGS libraries showed, that one out of 10 markers (mainly in the IGK, TRG and TRD loci) used for qPCR-MRD stratification did not provide satisfactory specificity, although they fully met EuroMRD criteria during the optimization of qPCR patient-specific assays. Our results show that NGS-MRD is highly concordant with traditional qPCR-based strategy and has comparable sensitivity and clinical value in the setting of a BFM-based clinical protocol, while being less laborious and providing significantly more specific results and additional information on the IG/TR repertoire (Kotrova et al, Blood 2016). Our study also emphasizes the importance of selecting MRD markers of adequate specificity at diagnosis. Currently, this selection can be assisted by these broad sequencing data on IG/TR repertoire of large number of patients. Based on these results, we propose that frontline NGS-MRD evaluation developed by the EuroClonality-NGS working group can be used as an alternative to traditional qPCR-based MRD quantitation in future MRD-based treatment protocols. Supported by grants NU20-03-00284 and NU20-07-00322 from the Czech Health Research Council and 534120 from Charles University. All methods were established through collaboration within the EuroClonality-NGS and EuroMRD groups. Disclosures van der Velden: Agilent: Research Funding; Navigate: Other: Service Level Agreement; Janssen: Other: Service Level Agreement; EuroFlow: Other: Service Level Agreement, Patents & Royalties: for network, not personally; BD Biosciences: Other: Service Level Agreement. Brüggemann: Incyte: Other: Advisory Board; Janssen: Speakers Bureau; Amgen: Other: Advisory Board, Travel support, Research Funding, Speakers Bureau. Langerak: Erasmus MS, University Medical Center: Current Employment; F. Hoffmann-La Roche Ltd/Genentech, Inc.: Research Funding; Gilead: Research Funding; Janssen: Speakers Bureau.

Higher Order Restrictions of the Immunoglobulin Repertoire in CLL: The Illustrative Case of Stereotyped Subsets #2 and #169

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5453-5453
Author(s):  
Katerina Gemenetzi ◽  
Andreas Agathangelidis ◽  
Fotis Psomopoulos ◽  
Karla Plevova ◽  
Lesley-Ann Sutton ◽  
...  
Keyword(s):  
Research Funding ◽  
High Throughput Sequencing ◽  
Somatic Hypermutation ◽  
Illustrative Case ◽  
Gene Rearrangements ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Mean Frequency ◽  
Cdr3 Sequence ◽  
Sample Range

Stereotyped subset #2 (IGHV3-21/IGLV3-21) is the largest subset in CLL (~3% of all patients). Membership in subset #2 is clinically relevant since these patients experience an aggressive disease irrespective of the somatic hypermutation (SHM) status of the clonotypic immunoglobulin heavy variable (IGHV) gene. Low-throughput evidence suggests that stereotyped subset #169, a minor CLL subset (~0.2% of all CLL), resembles subset #2 at the immunogenetic level. More specifically: (i) the clonotypic heavy chain (HC) of subset #169 is encoded by the IGHV3-48 gene which is closely related to the IGHV3-21 gene; (ii) both subsets carry VH CDR3s comprising 9-amino acids (aa) with a conserved aspartic acid (D) at VH CDR3 position 3; (iii) both subsets bear light chains (LC) encoded by the IGLV3-21 gene with a restricted VL CDR3; and, (iv) both subsets have borderline SHM status. Here we comprehensively assessed the ontogenetic relationship between CLL subsets #2 and #169 by analyzing their immunogenetic signatures. Utilizing next-generation sequencing (NGS) we studied the HC and LC gene rearrangements of 6 subset #169 patients and 20 subset #2 cases. In brief, IGHV-IGHD-IGHJ and IGLV-IGLJ gene rearrangements were RT-PCR amplified using subgroup-specific leader primers as well as IGHJ and IGLC primers, respectively. Libraries were sequenced on the MiSeq Illumina instrument. IG sequence annotation was performed with IMGT/HighV-QUEST and metadata analysis conducted using an in-house, validated bioinformatics pipeline. Rearrangements with identical CDR3 aa sequences were herein defined as clonotypes, whereas clonotypes with different aa substitutions within the V-domain were defined as subclones. For the HC analysis of subset #169, we obtained 894,849 productive sequences (mean: 127,836, range: 87,509-208,019). On average, each analyzed sample carried 54 clonotypes (range: 44-68); the dominant clonotype had a mean frequency of 99.1% (range: 98.8-99.2%) and displayed considerable intraclonal heterogeneity with a mean of 2,641 subclones/sample (range: 1,566-6,533). For the LCs of subset #169, we obtained 2,096,728 productive sequences (mean: 299,533, range: 186,637-389,258). LCs carried a higher number of distinct clonotypes/sample compared to their partner HCs (mean: 148, range: 110-205); the dominant clonotype had a mean frequency of 98.1% (range: 97.2-98.6%). Intraclonal heterogeneity was also observed in the LCs, with a mean of 6,325 subclones/sample (range: 4,651-11,444), hence more pronounced than in their partner HCs. Viewing each of the cumulative VH and VL CDR3 sequence datasets as a single entity branching through diversification enabled the identification of common sequences. In particular, 2 VH clonotypes were present in 3/6 cases, while a single VL clonotype was present in all 6 cases, albeit at varying frequencies; interestingly, this VL CDR3 sequence was also detected in all subset #2 cases, underscoring the molecular similarities between the two subsets. Focusing on SHM, the following observations were made: (i) the frequent 3-nucleotide (AGT) deletion evidenced in the VH CDR2 of subset #2 (leading to the deletion of one of 5 consecutive serine residues) was also detected in all subset #169 cases at subclonal level (average: 6% per sample, range: 0.1-10.8%); of note, the 5-serine stretch is also present in the germline VH CDR2 of the IGHV3-48 gene; (ii) the R-to-G substitution at the VL-CL linker, a ubiquitous SHM in subset #2 and previously reported as critical for IG self-association leading to cell autonomous signaling in this subset, was present in all subset #169 samples as a clonal event with a mean frequency of 98.3%; and, finally, (iii) the S-to-G substitution at position 6 of the VL CDR3, present in all subset #2 cases (mean : 44.2% ,range: 6.3-87%), was also found in all #169 samples, representing a clonal event in 1 case (97.2% of all clonotypes) and a subclonal event in the remaining 5 cases (mean: 0.6%, range: 0.4-1.1%). In conclusion, the present high-throughput sequencing data cements the immunogenetic relatedness of CLL stereotyped subsets #2 and #169, further highlighting the role of antigen selection throughout their natural history. These findings also argue for a similar pathophysiology for these subsets that could also be reflected in a similar clonal behavior, with implications for risk stratification. Disclosures Sutton: Abbvie: Honoraria; Gilead: Honoraria; Janssen: Honoraria. Stamatopoulos:Abbvie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Chatzidimitriou:Janssen: Honoraria.

scholarly journals High Levels of Circulating Tumor Cells Are Associated with Increased Bone Marrow Proliferation in Newly Diagnosed Multiple Myeloma Patients

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1566-1566
Author(s):  
Cathelijne Fokkema ◽  
Madelon M.E. de Jong ◽  
Sabrin Tahri ◽  
Zoltan Kellermayer ◽  
Chelsea den Hollander ◽  
...  
Keyword(s):  
Bone Marrow ◽  
High Risk ◽  
Single Cell ◽  
Tumor Cells ◽  
Research Funding ◽  
Immune Cell ◽  
Service Level Agreement ◽  
Service Level ◽  
Newly Diagnosed ◽  
Cell Data

Abstract Introduction The introduction of new treatment regimens has significantly increased the progression free survival (PFS) of newly diagnosed multiple myeloma (MM) patients. However, even with these novel treatments, for some the disease remains refractory, highlighting the need to identify the pathobiology of high-risk MM. In MM patients, high levels of circulating tumor cells (CTCs) is associated with an inferior prognosis independent of high-risk cytogenetics (Chakraborty et al., 2016), suggesting that CTC numbers are a relevant reflection of tumor cell biology. We hypothesized that high levels of CTCs in MM patients are either the result of a transcriptionally distinct tumor clone with enhanced migration capacities, or driven by transcriptional differences present in the bone marrow (BM) tumor cells. To test these hypotheses, we 1) compared MM cells from paired blood and BM samples, and 2) compared BM tumor cells of patients with high and low CTC levels, using single cell RNA-sequencing. Results We isolated plasma cell (PCs) from viably frozen mononuclear cells of paired peripheral blood (PB) and BM aspirates from five newly diagnosed MM patients (0.5%-8% CTCs) to determine the presence of a distinct CTC subclone. We generated single cell transcriptomes from 44,779 CTCs and 35,697 BM PCs. In the total 9 clusters common to BM PCs and CTCs were identified upon single cell data integration, but no cluster specific for either source was detected. Only 25 genes were significantly differential expressed between CTCs and BM PCs. The absence of transcriptional clusters unique to either CTCs or BM PCs, and the transcriptional similarity between these two anatomical sites makes it highly unlikely that CTC levels are driven by the presence of a transcriptionally-primed migratory clone. We next set out to identify possible transcriptional differences in BM PCs from eight patients with high (2-22%) versus thirteen patients with low (0.004%-0.08%) percentages of CTCs. Recurrent high-risk mutations were present in both groups. Single cell transcriptomes were generated from 74,830 BM PCs. Single cell data integration across all patients led to the identification of 8 distinct PC clusters, one of which was characterized by enhanced proliferation as defined by STMN1 and MKI67 transcription. Interestingly, this proliferative cluster was increased in patients with a high percentage of CTCs. Furthermore, cell cycle analyses based on canonical G2M and S phase markers revealed that actively cycling PCs were more frequent in the BM of patients with a high percentage of CTCs (64% versus 30%, p<0.001), irrespective of the transcriptional cluster of origin. We hypothesized that plasma cell-extrinsic cues from the bone marrow micro-environment might be driving tumor proliferation. In order to substantiate this, we isolated BM immune cells from the same 21 patients and generated a library of 301,045 single immune cell transcriptomes. This library contained all major immune cell subsets, including CD4 + and CD8 + T cells, NK cells, B cells and monocytes. Comparative analyses of these cell populations in patients with either high or low levels of CTC are ongoing. Conclusion Through single cell transcriptomic analyses, we demonstrate that CTCs and BM PCs are transcriptionally similar. Importantly, we identify increased BM PC proliferation as a significant difference between patients with high and low levels of CTCs, implicating an increased tumor proliferation as one of the potential mechanisms driving CTC levels and MM disease pathobiology. The relation of the BM immune micro-environment to this altered proliferative state is currently under investigation. Disclosures van der Velden: Janssen: Other: Service Level Agreement; BD Biosciences: Other: Service Level Agreement; Navigate: Other: Service Level Agreement; Agilent: Research Funding; EuroFlow: Other: Service Level Agreement, Patents & Royalties: for network, not personally. Sonneveld: SkylineDx: Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene/BMS: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Research Funding. Broyl: Sanofi: Honoraria; Janssen Pharmaceuticals: Honoraria; Celgene: Honoraria; Bristol-Meyer Squibb: Honoraria; Amgen: Honoraria.

Patient-Specific Mutation-Derived Tumor Antigens As Targets for Cancer Immunotherapy in Multiple Myeloma

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1851-1851
Author(s):  
Deepak Perumal ◽  
Alessandro Lagana' ◽  
Alex Rubinsteyn ◽  
John P Finnigan ◽  
Pei-Yu Kuo ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
In Silico ◽  
Research Funding ◽  
Cell Activation ◽  
Somatic Mutations ◽  
Sequence Data ◽  
Advisory Board ◽  
Patient Specific ◽  
Scientific Advisory Board ◽  
Scientific Advisory

Abstract Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for more than 10,000 deaths in the US each year. Novel therapeutic approaches for relapsed MM are urgently needed. Tumor-specific mutations are ideal targets for cancer immunotherapy as they can be potentially recognized as neo-antigens by mature T-cells. Targeting tumor-specific antigens harboring somatic mutations presented on major histocompatibility complex class I molecules (MHC-I) with peptides could personalize the therapeutic approach for relapsed patients. To test this possibility, we examined 6 relapsed MM tumor samples from Mount Sinai, NY to predict in silico patient-specific tumor mutations that may activate the patient's immune systems. This is the first study to utilize Whole-Exome Sequence data (WES) from relapsed MM patients to show the feasibility of using exome sequencing to identify mutation derived neo-antigens that are patient-specific. DNA and RNA from six MM patients were extracted from sorted CD138+ cells from bone marrow aspirates. At the time of sample collection all patients had relapsed following at least five lines of therapy including Autologous Stem Cell Transplantation. The exome capture for DNA sequencing was carried out using the Agilent human whole-exome SureSelect assay. RNA-seq libraries were prepared using Illumina mRNA-seq protocol. All libraries were sequenced on an Illumina HiSeq2500 to generate 100 nucleotide reads. RNA reads were aligned to human reference genome (hg19) and assembled into transcripts using Bowtie-TopHat-Cufflinks. WES data was mapped to reference genome by BWA and then processed by MuTect to detect somatic mutations. Patient-specific alleles were determined using Seq2HLA. The identified mutations lead to candidate antigenic peptides that were filtered by tumor expression level (FPKM >2) using RNA sequence data. Candidate peptides of 8-11 character long were then ranked based on peptide-MHC binding affinity prediction (IC50nM) performed in silico using NetMHCpan. We identified a total of 340 tumor-specific nonsynonymous somatic mutations expressed in the context of patient specific HLA type. 263 (77%) genes were strong binders (IC50<150nM) and 77 (23%) genes were moderate/weak binders (IC50150-500nM). The number of mutated genes that were immunogenic per patient ranged from a minimum of 6 genes to 147 genes. Further, Database for Annotation, Visualization and Integrated Discovery (DAVID) tool was used to identify potentially enriched biological processes among the 340 genes using Gene Ontology (GO) terms. Enrichment analysis of 263 genes showed that they are mainly involved in myeloid cell activation during immune response (eg.LAT2, MYO1F), cell cycle process (CDK1, CHEK2, DNM2, EP300, SETD8), cellular response to stress (RAD21, HDAC2, MAT2B), chromatin silencing (SIRT2, SMARCA4), cell apoptosis and signal transduction (KRAS, NLRP1, ING4, IGF2R). Similarly enrichment analysis of 77 genes revealed their involvement mainly in B cell activation and leukocyte differentiation (LRRC8A, CD3E, PRKDC). Examples of some of these significantly mutated genes with binding affinity and predicted peptides are shown in Table 1. In this study, we show for the first time a correlation between tumor mutations and the epitope landscape by in silico data, suggesting that somatic mutations in MM are immunogenic and could potentially confer antitumor vaccine activity. Our results support an approach in creating cancer vaccines that use tumor-specific immunogenic mutations for the development of personalized vaccines for MM patients. Table 1. Immunogenic mutations in Multiple Myeloma # Patient Specific alleles Peptides IC50 Mutated Genes Effect Patient#1 1 HLA-C*14:02 CYGHTMVAF 57.75 LZTR1 p.R284C 2 HLA-C*14:02 LYFFGMHVQEY 29.75 EP300 p.C1372Y 3 HLA-C*14:02 TFNEPSSEYF 114.21 SMARCA4 p.G1146S Patient#2 4 HLA-B*15:01 MSLHNLGTVF 26.2 BCR p.A1160G 5 HLA-A*31:01 SIISDSPR 149.38 FcRL5 p.V269I 6 HLA-A*31:01 HYFMHLLK 37.16 SIRT2 p.R116H Patient#3 7 HLA-C*05:01 ITDFGHSEIL 25.18 CHEK2 p.K344E Patient#4 8 HLA-A*11:01 VVGARGVGK 121.47 KRAS p.G12R 9 HLA-B*41:01 LEIDQLFRI 132.84 CDK1 p.S208L Patient#5 10 HLA-A*31:01 AVGCGFRRARR 106.68 MAT2B p.P65R Patient#6 11 HLA-A*30:01 HQRVLYIEI 93.61 HDAC2 p.D145E 12 HLA-A*30:01 FTRCLTPLL 63.19 RAD21 p.V397L Disclosures Chari: Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Biotest: Other: Institutional Research Funding; Novartis: Consultancy, Research Funding; Onyx: Consultancy, Research Funding. Jagannath:BMS: Honoraria; Janssen: Honoraria; MERCK: Honoraria; Novartis Pharmaceuticals Corporation: Honoraria; Celgene: Honoraria. Dudley:NuMedii, Inc: Patents & Royalties; Janssen Pharmaceuticals: Consultancy; GlaxoSmithKline: Consultancy; Personalis: Patents & Royalties; Ayasdi, Inc: Other: Equity; Ecoeos, Inc: Other: Equity. Hammerbacher:Cloudera: Membership on an entity's Board of Directors or advisory committees; Bay Sensors: Other: Equity; Cambrian Genomics: Other: Equity; Genome Compiler Corporation: Other: Equity; Science Exchange: Other: Equity; Transcriptic: Other: Equity; Pymetrics: Other: Equity. Schadt:Pacific Biosciences: Consultancy; Berg Pharma: Other: Scientific Advisory Board; GNS Healthcare: Other: Scientific Advisory Board; Clinical Gene Networks AB: Other: Equity. Bhardwaj:Dynavax Technologies Corporation: Consultancy; Crucell: Other: Equity; Dendreon Corporation: Other: Scientific Advisory Board; Merck & Co., Inc.: Other: Scientific Advisory Board; Neostem, Inc.: Other: Equity.

Phase I Study of Ruxolitinib for Patients (Pts) with Low or Intermediate-1 Risk Myelodysplastic Syndrome (MDS) Who Failed at Least One Line of Therapy

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4318-4318 ◽  
Author(s):  
Yasmin Abaza ◽  
Elias J. Jabbour ◽  
Srdan Verstovsek ◽  
Zeev Estrov ◽  
Farhad Ravandi ◽  
...  
Keyword(s):  
Dose Level ◽  
Dose Escalation ◽  
Lower Risk ◽  
Research Funding ◽  
Advisory Board ◽  
Sequencing Data ◽  
Jak2 Mutation ◽  
Line Of Therapy ◽  
Dose Levels

Abstract Background: Prognosis of lower risk MDS pts who failed one line of therapy is heterogeneous. Median overall survival (OS) of lower risk MDS pts who failed hypomethylating agents (HMAs) is about 17 months. Several studies have indicated that deregulation of pathways involved in innate immunity signaling is frequent in lower risk MDS. This process involves Toll-like receptor activation, cytokine overexpression, and NF-kB activation which parallels activation of the JAK-STAT system. We hypothesized that ruxolitinib, a JAK2 inhibitor, could have activity in a subset of lower risk MDS pts. Methods: We designed a pilot phase I trial of dose escalation of ruxolitinib for pts with previously treated MDS classified as Low or Intermediate-1 by the International Prognostic Scoring System (IPSS) and signs of JAK-STAT activation determined by either: elevated β2-microglobulin (B2M) levels x2 upper limit of normal, presence of a JAK2 mutation, or presence of phosphorylated p65 (pp65) NF-κB component in at least 5% of bone marrow (BM) cells. Sequencing data was obtained at the time of pre-treatment evaluation by the use of a 28-gene next generation sequencing platform. The primary objective was to determine the maximum tolerated dose (MTD) and dose limiting toxicity (DLT) of ruxolitinib. The secondary objective was to determine the clinical and molecular activity of ruxolitinib. Responses were based on the 2006 International Working Group criteria. Pts were assigned to 4 dose levels [ruxolitinib 5, 10, 15, and 20 mg administered orally twice daily (BID) on a 28 day cycle] using a 3+3 dose-escalation design to determine the MTD. DLTs were evaluated during the first two cycles of therapy. After completion of the first dose level, pts at subsequent dose levels received the prior dose level for 1 month with dose escalation on the second month (e.g. in dose level 2, pts received 5 mg in cycle 1 and 10 mg in cycle 2). Results: Fourteen pts have been enrolled so far (4 CMML, 10 MDS); 3 pts (21 %) classified as low-risk and 11 (79 %) as intermediate-1-risk by IPSS. All pts were evaluable for toxicity and response. Patient characteristic are shown in Table 1. Median age was 70 years (range, 53-83). Ten pts had diploid cytogenetics (71%) and 4 pts had intermediate karyotype. Sequencing data was available in 10 (71%) pts. Inclusion criteria included elevated B2M in 2 (14%) pts, phosphorylated p65 in 9 (64%), and presence of JAK2 mutation in 3 (21%). Among pts with detectable bone marrow pp65, mean pp65 positivity was 28% (14-42%). Additional mutations were detected in 4 pts including NRAS (N=2), TP53 (N=1), and one pt had ASXL1, KRAS, and TET2 mutations. Median number of cycles of ruxolitinib was 3 (range, 2-14). There were no dose reductions with only one pt requiring treatment interruption. No DLT was observed and the MTD was ruxolitinib 20 mg BID. Grade 3-4 toxicities occurred in 11 pts (79%) with the most common being thrombocytopenia (N=5, 36%) and anemia (N=7, 50%). The overall response rate was 21% (3/14), with 2 pts achieving hematological improvement in platelets (HI-P) and 1 pt achieving partial cytogenetic response. Median time to response was 8 weeks (range 4-8 weeks). Among pts with detectable pp65 at the time of initial evaluation, a trend to reduced pp65 expression after therapy was observed, with a mean pp65 expression after therapy of 26% (12-39%, p=0.81). Median follow up was 8.5 months (range 2-24 months) with median OS not having been reached at the current time of follow up. A total of 5 (36%) pts remain on study and have received a median of 4 cycles (range 2-14); 4 have no response (NR) so far and 1 achieved HI-P. Among responders, 2 pts had progression of disease (response duration was 1 and 4 months, respectively) and 1 remains in HI-P after 11 months of follow up. A total of 2 (14%) pts were taken of study due to progression (including transformation to AML in one patient), 5 (36%) due to absence of response, 1 (7%) to proceed to allogeneic stem-cell transplantation (SCT) and 1 (7%) due to development of a second malignancy. Two pts received SCT; 1 had NR to ruxolitinib after 2 cycles and the other transformed to AML, both alive post-SCT. Three pts (21 %) died; 2 due to AML progression and 1 from unrelated stroke complications. Conclusion: Ruxolitinib is well tolerated and active in lower-risk MDS pts with cytokine dysregulation. Although correlative studies show a trend towards reduction in pp65 expression, this did not correlate with response to ruxolitinib. Disclosures Jabbour: ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Daver:Kiromic: Research Funding; Ariad: Research Funding; Pfizer: Consultancy, Research Funding; Sunesis: Consultancy, Research Funding; Karyopharm: Honoraria, Research Funding; BMS: Research Funding; Otsuka: Consultancy, Honoraria. DiNardo:Agios: Other: advisory board, Research Funding; Celgene: Research Funding; Abbvie: Research Funding; Daiichi Sankyo: Other: advisory board, Research Funding; Novartis: Other: advisory board, Research Funding.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

2021 ◽  
Vol 18 (3) ◽  
pp. 1327
Author(s):  
Leonardo J. Gutierrez ◽  
Kashif Rabbani ◽  
Oluwashina Joseph Ajayi ◽  
Samson Kahsay Gebresilassie ◽  
Joseph Rafferty ◽  
...  
Keyword(s):  
Mental Health ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Identity Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Self Organization ◽  
Future Research ◽  
Study Results ◽  
Open Issues

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

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