LEADER 10123nam 2200565 450 001 9910495203303321 005 20230927141827.0 010 $a3-030-73440-4 035 $a(CKB)4100000011990201 035 $a(MiAaPQ)EBC6683053 035 $a(Au-PeEL)EBL6683053 035 $a(PPN)260305456 035 $a(EXLCZ)994100000011990201 100 $a20220413d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aResistance to targeted therapies in multiple myeloma /$fSilvia Cw Ling, Steven Trieu, editors 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (161 pages) 225 1 $aResistance to Targeted Anti-Cancer Therapeutics ;$vVolume 22 311 $a3-030-73439-0 327 $aIntro -- Aims and Scope -- Objective -- Preface -- Contents -- Series Editor Biography -- About the Series Editor -- Contributors -- About the Editors -- Chapter 1: The Role of Targeted Therapy in Multiple Myeloma -- 1.1 Multiple Myeloma Overview -- 1.2 Historical Treatment of Multiple Myeloma Until Present -- 1.3 Immunomodulatory Imide Drugs -- 1.4 Proteasome Inhibitors -- 1.5 Monoclonal Antibodies -- 1.6 Histone Deacetylase Inhibitors -- 1.7 Bone Targeted Therapy -- 1.8 New Agents on the Horizon -- 1.9 Conclusion -- References -- Chapter 2: Lenalidomide -- 2.1 Introduction -- 2.2 Indications -- 2.3 Efficacy of Lenalidomide -- 2.3.1 Efficacy in Relapsed or Refractory Multiple Myeloma -- 2.3.1.1 Lenalidomide and Dexamethasone -- 2.3.1.2 Bortezomib, Lenalidomide, and Dexamethasone -- 2.3.1.3 Daratumumab, Lenalidomide, and Dexamethasone -- 2.3.1.4 Carfilzomib, Lenalidomide, and Dexamethasone -- 2.3.2 Efficacy in Newly Diagnosed Multiple Myeloma -- 2.3.2.1 Transplant Ineligible Patients -- Lenalidomide and Dexamethasone -- Cyclophosphamide, Lenalidomide, and Dexamethasone -- Bortezomib, Lenalidomide, and Dexamethasone -- 2.3.2.2 Transplant Eligible Patients -- Bortezomib, Lenalidomide, and Dexamethasone -- 2.4 Mechanisms of Action -- 2.4.1 Cereblon Pathway -- 2.4.2 Effect on Cytokines -- 2.4.3 T Cell Activation -- 2.4.4 Effect on Natural Killer Cells -- 2.4.5 Anti-Angiogenic Activity -- 2.4.6 Direct Antitumor Activity -- 2.4.7 Myeloma Microenvironment -- 2.5 Lenalidomide Resistance -- 2.5.1 Potential Mechanisms of Lenalidomide Resistance -- 2.5.1.1 Decreased Cereblon Expression and Downstream Factors -- 2.5.1.2 Increase in c-Myc -- 2.5.2 Management of Lenalidomide-Resistant Disease -- 2.5.2.1 Pomalidomide-Based Regimes -- 2.5.2.2 Proteasome Inhibitor and Daratumumab-Based Regimes -- 2.6 Conclusion -- References -- Chapter 3: Pomalidomide. 327 $a3.1 Introduction -- 3.2 Clinical Indication of Pomalidomide -- 3.3 Efficacy -- 3.3.1 Efficacy in Relapsed and Refractory Multiple Myeloma -- 3.3.1.1 Pomalidomide and Dexamethasone -- 3.3.1.2 Pomalidomide + Dexamethasone + Cyclophosphamide -- 3.3.1.3 Pomalidomide, Bortezomib, and Dexamethasone -- 3.3.1.4 Pomalidomide, Daratumumab, and Dexamethasone -- 3.3.1.5 Pembrolizumab, Pomalidomide, and Dexamethasone -- 3.4 Mechanisms of Pomalidomide Action -- 3.5 Potential Mechanism of Pomalidomide Resistance and Overcoming Resistance -- 3.6 Conclusion -- References -- Chapter 4: Mechanisms Driving Resistance to Proteasome Inhibitors Bortezomib, Carfilzomib, and Ixazomib in Multiple Myeloma -- 4.1 Introduction -- 4.2 The Proteasome -- 4.3 Endoplasmic Reticulum Stress -- 4.4 Proteasome Inhibitors in Multiple Myeloma -- 4.5 Bortezomib Resistance Mechanisms -- 4.5.1 Proteasome Mutation and Overexpression -- 4.5.2 Drug Efflux -- 4.5.3 Plasma Cell Differentiation -- 4.5.4 Upregulation of Heat Shock Proteins -- 4.5.5 Autophagy -- 4.5.6 The Bone Marrow Microenvironment -- 4.6 Resistance Mechanisms to Second Generation Proteasome Inhibitors -- 4.6.1 Carfilzomib Resistance Mechanisms -- 4.6.1.1 Proteasome Mutations -- 4.6.1.2 Drug Efflux -- 4.6.1.3 Autophagy -- 4.6.1.4 Bone Marrow Microenvironment -- 4.6.2 Ixazomib Resistance Mechanisms -- 4.7 Conclusion -- References -- Chapter 5: Daratumumab -- 5.1 Introduction -- 5.2 Mechanism of Action -- 5.3 Mechanisms Behind Daratumumab-Resistance -- 5.3.1 Reduced Cell Surface Expression of Target Antigen CD38 -- 5.3.2 Antibody-Dependent Cell Cytotoxicity Resistance -- 5.3.3 Antibody-Dependent Cellular Phagocytosis Resistance -- 5.3.4 Complement-Dependent Cytotoxicity Resistance -- 5.3.5 Immune Modulated Resistance -- 5.4 Clinical Efficacy of Daratumumab -- 5.4.1 Daratumumab in the Relapsed and Refractory Setting. 327 $a5.4.2 Daratumumab in Newly Diagnosed, Transplant Ineligible Patients -- 5.4.3 Daratumumab in Newly Diagnosed, Transplant Eligible Patients -- 5.5 Toxicity Profile -- 5.6 Conclusion -- References -- Chapter 6: Elotuzumab -- 6.1 Introduction -- 6.2 Mechanisms of Action -- 6.2.1 Preclinical Studies -- 6.3 Pharmacological Characteristics of Elotuzumab -- 6.4 Mechanisms of Resistance to Elotuzumab -- 6.4.1 Expression of the Antigen Target of the Monoclonal Antibody -- 6.4.2 CD16a Expression on NK Cells and Associated Polymorphisms -- 6.4.3 Interactions with the Microenvironment -- 6.4.4 Development of Neutralizing Antibodies -- 6.5 Clinical Trials -- 6.5.1 Relapsed and/or Refractory Myeloma -- 6.6 Toxicities of Elotuzumab -- 6.7 Conclusion -- References -- Chapter 7: Histone Deacetylase Inhibitors -- 7.1 Introduction -- 7.2 Histone Deacetylases -- 7.2.1 Class I Histone Deacetylases -- 7.2.2 Class II Histone Deacetylases -- 7.2.3 Class III Histone Deacetylases (Sirtuins) -- 7.2.4 Class IV Histone Deacetylases -- 7.3 Histone Deacetylases in Multiple Myeloma -- 7.3.1 Histone Deacetylases and Protein Clearance -- 7.3.2 Histone Deacetylase Overexpression and Increased Activity in Multiple Myeloma -- 7.4 Histone Deacetylase Inhibitors -- 7.4.1 Types of Histone Deacetylase Inhibitors -- 7.4.2 Mechanisms of Action -- 7.4.2.1 Altered Gene Expression -- 7.4.2.2 Induction of Apoptosis -- 7.4.2.3 Cell Cycle Arrest -- 7.4.2.4 Inhibition of Angiogenesis -- 7.4.2.5 Regulation of Cytokines -- 7.4.2.6 Suppressed DNA Damage Repair -- 7.4.2.7 Ubiquitin Proteasome System -- 7.4.2.8 Aggresome Pathway -- 7.5 Approved Histone Deacetylase Inhibitors -- 7.5.1 Panobinostat -- 7.5.2 Vorinostat -- 7.5.3 Ricolinostat -- 7.6 Immunomodulatory Imide Drugs and Histone Deacetylase Inhibitors -- 7.7 Potential Mechanisms of Resistance to Histone Deacetylase Inhibitors. 327 $a7.7.1 Drug Transporters -- 7.7.2 Cell Signaling -- 7.7.3 Antioxidant Pathway -- 7.7.4 Cell Cycle Proteins -- 7.7.5 Nuclear Factor-Kappa B -- 7.7.6 Anti-Apoptotic Proteins -- 7.7.7 Altered Histone Deacetylases -- 7.7.8 Autophagy -- 7.8 Conclusion -- References -- Chapter 8: Bone Targeted Therapies -- 8.1 Myeloma Bone Disease -- 8.1.1 Diagnosis -- 8.1.2 Pathogenesis -- 8.1.3 Osteoclastic Activation -- 8.1.3.1 The RANK/RANKL Pathway -- 8.1.3.2 Interleukins -- 8.1.3.3 Hepatocyte Growth Factor -- 8.1.3.4 Notch Pathway -- 8.1.3.5 Chemokines -- 8.1.3.6 Activin A -- 8.1.3.7 The TNF Superfamily -- 8.1.3.8 BTK and SDF-1? -- 8.1.4 Osteoblastic Suppression -- 8.1.4.1 The WNT Pathway -- 8.1.4.2 Sclerostin -- 8.1.4.3 DKK1 -- 8.1.4.4 Periostin -- 8.1.4.5 RUNX2/CBFA1 and IL-7 -- 8.2 Indications for Bone Targeted Therapies -- 8.3 Utility of Bone Resorption Markers to Guide Therapy -- 8.4 Current Treatments for Myeloma Bone Disease -- 8.4.1 Bisphosphonates -- 8.4.1.1 Mechanism of Action -- 8.4.1.2 Evidence in MBD -- 8.4.1.3 Comparison Between Bisphosphonates -- 8.4.1.4 Adverse Events -- 8.4.1.5 Renal Impairment -- 8.4.1.6 Osteonecrosis of the Jaw -- 8.4.1.7 Subtrochanteric and Other Atypical Femoral Fractures -- 8.4.1.8 Duration, Frequency, and Monitoring of Therapy -- 8.4.1.9 Future of Bisphosphonate Therapy -- 8.4.2 Denosumab -- 8.4.2.1 Mechanism of Action -- 8.4.2.2 Evidence in MBD -- 8.4.2.3 Adverse Events -- 8.4.2.4 Duration and Frequency of Therapy -- 8.4.3 Novel Therapies -- 8.4.3.1 Anti-Sclerostin Antibodies -- 8.4.3.2 Anti-DKK1 Neutralizing Antibodies -- 8.4.3.3 Activin Receptor Ligand Traps -- 8.4.3.4 Bruton's Tyrosine Kinase (BTK) Inhibitors -- 8.4.3.5 B Cell Activating Factor (BAFF) Neutralizing Antibodies -- 8.4.3.6 Transforming Growth Factor-? (TGF-?) Inhibitors -- 8.4.3.7 Parathyroid Hormone -- 8.4.3.8 Proteasome Inhibitors. 327 $a8.4.3.9 Immunomodulatory Imide Drugs -- 8.5 Conclusion -- References -- Chapter 9: New Targeted Therapies for Multiple Myeloma Under Clinical Investigation -- 9.1 Introduction -- 9.2 Antibodies -- 9.2.1 Monoclonal Antibodies Directed at Plasma Cells -- 9.2.2 Bispecific Antibodies -- 9.2.3 Checkpoint Inhibitor -- 9.2.4 Cellular Therapies -- 9.2.4.1 Chimeric Antigen Receptor T Cells -- 9.2.4.2 DC Vaccination -- 9.2.5 Small-Molecule Inhibitors -- 9.2.5.1 Targeting Specific Subsets of Patients -- Venetoclax for Patients with Chromosomal Translocation t(11 -- 14) -- Patients with BRAF V600E Mutation -- Potential MEK Inhibition -- 9.2.5.2 Patients with Overexpressed FGFR3 -- 9.2.5.3 Targeting Inherent Weaknesses in MM -- SINE Compounds -- Bromodomain Inhibitors -- Kinesin Spindle Protein Inhibitors -- 9.3 Conclusion and Perspectives -- References -- Index. 410 0$aResistance to targeted anti-cancer therapeutics ;$vVolume 22. 606 $aDrug resistance in cancer cells 606 $aMultiple myeloma$xTreatment 606 $aMieloma múltiple$2thub 606 $aResistència als medicaments$2thub 608 $aLlibres electrònics$2thub 615 0$aDrug resistance in cancer cells. 615 0$aMultiple myeloma$xTreatment. 615 7$aMieloma múltiple 615 7$aResistència als medicaments 676 $a616.994061 702 $aLing$b Silvia Cw 702 $aTrieu$b Steven 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910495203303321 996 $aResistance to Targeted Therapies in Multiple Myeloma$92272602 997 $aUNINA LEADER 08320nam 22007095 450 001 9910484222503321 005 20251113174951.0 010 $a3-030-75765-X 024 7 $a10.1007/978-3-030-75765-6 035 $a(CKB)4100000011918703 035 $a(DE-He213)978-3-030-75765-6 035 $a(MiAaPQ)EBC6607519 035 $a(Au-PeEL)EBL6607519 035 $a(OCoLC)1250275609 035 $a(PPN)255881436 035 $a(EXLCZ)994100000011918703 100 $a20210507d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Data Mining $e25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11?14, 2021, Proceedings, Part II /$fedited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XXVI, 774 p. 30 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12713 311 08$a3-030-75764-1 327 $aClassical Data Mining,. Mining Frequent Patterns from Hypergraph Databases -- Discriminating Frequent Pattern based Supervised Graph Embedding for Classification -- Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure -- Similarity Forest Revisited: a Swiss Army Knife for Machine Learning -- Discriminative Representation Learning for Cross-domain Sentiment Classification -- SAGCN: Towards Structure-Aware Deep Graph Convolutional Networks on Node Classification -- Hierarchical Learning of Dependent Concepts for Human Activity Recognition -- Improving Short Text Classification Using Context-Sensitive Representations and Content-Aware Extended Topic Knowledge -- A Novel Method for Offline Handwritten Chinese Character Recognition under the Guidance of Print -- Upgraded Attention-based Local FeatureLearning Block for speech emotion recognition -- Memorization in Deep Neural Networks: Does the Loss Function matter -- Gaussian Soft Decision Trees for Interpretable Feature-Based Classification -- Efficient Nodes Representation Learning with Residual Feature Propagation -- Progressive AutoSpeech: An efficient and general framework for automatic speech classification -- CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data -- Effective and Adaptive Multi-metric Refined Similarity Graph Fusion for Multi-view Clustering -- aHCQ: Adaptive Hierarchical Clustering based Quantization Framework for Deep Neural Networks -- Maintaining Consistency with Constraints: a Constrained Deep Clustering method -- Data Mining Theory and Principles -- Towards multi-label Feature selection by Instance and Label Selections -- FARF: A Fair and Adaptive Random Forests Classifier -- Sparse Spectrum Gaussian Process for Bayesian Optimization -- Densely Connected Graph Attention Network based on Iterative Path Reasoning for Document-level Relation Extraction -- Causal Inference Using Global Forecasting Models for Counterfactual Prediction. -CED-BGFN: Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network -- Learning Finite Automata with Shuffle -- Active Learning based Similarity Filtering for Efficient and Effective Record Linkage -- Stratified Sampling for Extreme Multi-Label Data -- Vertical Federated Learning for Higher-order Factorization Machines -- dK-Projection: Publishing Graph Joint degree distribution with Node Differential Privacy -- Recommender Systems -- Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks -- Exploring Implicit Relationships in Social Network for Recommendation Systems -- Transferable Contextual Bandits with Prior Observations -- Modeling Hierarchical Intents and Selective Current Interest for Session-based Recommendation -- A Finetuned language model for Recommending cQA-QAs for enriching Textbooks -- XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction -- Learning Multiclass Classifier Under Noisy Bandit Feedback -- Diversify orNot: Dynamic Diversification for Personalized Recommendation -- Multi-criteria and Review-based Overall Rating Prediction -- W2FM: The Doubly-Warped Factorization Machine -- Causal Combinatorial Factorization Machines for Set-wise Recommendation -- Transformer-based Multi-task Learning for Queuing Time Aware Next POI Recommendation -- Joint Modeling Dynamic Preferences of Users and Items Using Reviews for Sequential Recommendation -- Box4Rec: Box Embedding for Sequential Recommendation -- UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering -- IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation -- Nonlinear Matrix Factorization via Neighbor Embedding -- Deconfounding representation learning based on user interactions in Recommendation Systems -- Personalized Regularization Learning for Fairer Matrix Factorization -- Instance Selection for Online Updating in Dynamic Recommender Environments -- Text Analytics.-Fusing Essential Knowledge for Text-Based Open-Domain Question Answering. - TSSE-DMM: Topic Modeling for Short Texts based on Topic Subdivision and Semantic Enhancement -- SILVER: Generating Persuasive Chinese Product Pitch -- Capturing SQL Query Overlapping via SubtreeCopy for Cross-domain Context-dependent SQLGeneration -- HScodeNet: Combining Hierarchical Sequential and Global Spatial Information of Text for Commodity HS Code Classification -- PLVCG: A Pretraining Based Model for Live Video Comment Generation -- Inducing Rich Interaction Structures between Words for Document-level Event Argument Extraction -- Exploiting Relevant Hyperlinks in Knowledge Base for Entity Linking -- TANTP: Conversational Emotion Recognition Using Tree-Based Attention Networks with Transformer Pre-training -- Semantic-syntax Cascade Injection Model for Aspect Sentiment Triple Extraction -- Modeling Inter-Aspect Relationship with Conjunction for Aspect-based Sentiment Analysis. 330 $aThe 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v12713 606 $aArtificial intelligence 606 $aSocial sciences$xData processing 606 $aEducation$xData processing 606 $aApplication software 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aArtificial Intelligence 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputers and Education 606 $aComputer and Information Systems Applications 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 615 0$aArtificial intelligence. 615 0$aSocial sciences$xData processing. 615 0$aEducation$xData processing. 615 0$aApplication software. 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 14$aArtificial Intelligence. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputers and Education. 615 24$aComputer and Information Systems Applications. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 676 $a006.3 702 $aKamalakar$b Karlapalem 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484222503321 996 $aAdvances in Knowledge Discovery and Data Mining$9772012 997 $aUNINA