Use Of Single-cell RNA Sequencing Research Articles

Single-Cell RNA Sequencing in Organ and Cell Transplantation.

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Current Knowledge and Use of Single-Cell RNA Sequencing in Osteoarthritis

Osteoarthritis (OA) is the most common chronic joint disease and it may progressively cause disability and compromise quality of life. Lately, the role of single cell RNA sequencing in the pathogenesis of OA has drawn a lot of attention. Number of literatures were studied, reviewed and analysed to write this review. In this review, use and knowledge on sc-RNA in OA will be outlined. Overall, the use of scRNAseq has enabled a more comprehensive understanding of the diverse cell populations and molecular signaling pathways involved in osteoarthritis pathogenesis.

Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies.

With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types. We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases. We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types. Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35461 and 23378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation. This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

Advancing parathyroid anatomy understanding through single-cell RNA sequencing in uremic secondary hyperparathyroidism

This commentary explores the recent application of single-cell RNA sequencing in the study of uremic secondary hyperparathyroidism, shedding light on the cellular dynamics within parathyroid glands. The use of single-cell RNA sequencing reveals new insights into the differentiation processes of chief and oxyphil cells, challenging traditional views and highlighting the potential of this technology in advancing our understanding of parathyroid anatomy.

T-cell infiltration and its regulatory mechanisms in cancers: insights at single-cell resolution.

Tumor-infiltrating T cells recognize, attack, and clear tumor cells, playing a central role in antitumor immune response. However, certain immune cells can impair this response and help tumor immune escape. Therefore, exploring the factors that influence T-cell infiltration is crucial to understand tumor immunity and improve therapeutic effect of cancer immunotherapy. The use of single-cell RNA sequencing (scRNA-seq) allows the high-resolution analysis of the precise composition of immune cells with different phenotypes and other microenvironmental factors, including non-immune stromal cells and the related molecules in the tumor microenvironment of various cancer types. In this review, we summarized the research progress on T-cell infiltration and the crosstalk of other stromal cells and cytokines during T-cell infiltration using scRNA-seq to provide insights into the mechanisms regulating T-cell infiltration and contribute new perspectives on tumor immunotherapy.

The use of single-cell RNA-seq to study heterogeneity at varying levels of virus-host interactions.

The outcome of viral infection depends on the diversity of the infecting viral population and the heterogeneity of the cell population that is infected. Until almost a decade ago, the study of these dynamic processes during viral infection was challenging and limited to certain targeted measurements. Presently, with the use of single-cell sequencing technology, the complex interface defined by the interactions of cells with infecting virus can now be studied across the breadth of the transcriptome in thousands of individual cells simultaneously. In this review, we will describe the use of single-cell RNA sequencing (scRNA-seq) to study the heterogeneity of viral infections, ranging from individual virions to the immune response between infected individuals. In addition, we highlight certain key experimental limitations and methodological decisions that are critical to analyzing scRNA-seq data at each scale.

Biomarkers for immunotherapy of hepatocellular carcinoma.

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.

Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges

Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.

Cell type-specific expression of the molecular players in mouse prefrontal cortex during cocaine addiction

Cocaine addiction is an issue that affects more than 5 million people in America per year. Although there has been much research into the genes and chemicals responsible for cocaine addiction, there are many specific questions left unanswered. Our experiment attempts to further previous research into certain molecular players. We follow up with their use of single-cell RNA sequencing on the prefrontal cortex cells of mice undergoing cocaine intravenous self-administration. Data of 12 samples from both saline and cocaine treated mice which are found on the Gene Expression Omnibus public database were retrieved. Using the Seurat function of RStudio, the data was merged into objects, normalized, clustered, and labeled into one of eight cell types. What resulted was a detailed UMAP plot displaying the clusters, their gene expression level, expression frequency, and their cell type. With this plot, we were able to determine the specific cell types that express the genes encoding the pre-established molecular players (∆FosB, MeCP2, and BDNF). When the analysis was expanded to a cell-type specific level, it was discovered some of these genes were selectively expressed in excitatory neurons and non-neuronal cells. Going further into the analysis, we determined the 6 genes with the most varied gene expression over the 3 stages of cocaine addiction for each of the 8 cell types. Overall, our computational analysis of publicly available transcriptome datasets from mouse addiction model provides new insights into the molecular basis of cocaine addiction.

Innovations advancing our understanding of microglia in Alzheimer's disease: From in vitro to in vivo models.

Microglia have been implicated in Alzheimer's disease (AD) pathogenesis through the identification of risk factor genes that are specifically or predominantly expressed in this cell type. Additional evidence suggests that microglia undergo dramatic morphological and phenotypic state changes during AD progression, as observed in human post-mortem tissue and animal model research. Although valuable, these studies are often hampered by either representing one time point in human tissue (end point) or because of the lack of conservation between species of microglial transcriptomes, proteomes and cell states. Thus, the development and application of novel human model systems have been beneficial in the study of microglia in neurodegeneration. Recent innovations include the use of human pluripotent stem cell (hPSC)-derived microglia in 2D or 3D culture systems, the transdifferentiation of microglia from patient monocytes and the xenotransplantation of hPSC-derived microglia into mouse brains. This review summarizes the recent innovations that have advanced our understanding of microglia in AD, through the use of single-cell RNA sequencing, hPSC-derived microglia culture within brain organoids and xenotransplantation into mouse brain. Through outlining the strengths and limitations of these approaches, we provide recommendations that will aid future endeavours in advancing our understanding of the complex role of microglia in AD onset and progression.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

Single-Cell RNA Sequencing for Studying Human Cancers.

Since the first publication a decade ago describing the use of single-cell RNA sequencing (scRNA-seq) in the context of cancer, over 200 datasets and thousands of scRNA-seq studies have been published in cancer biology. scRNA-seq technologies have been applied across dozens of cancer types and a diverse array of study designs to improve our understanding of tumor biology, the tumor microenvironment, and therapeutic responses, and scRNA-seq is on the verge of being used to improve decision-making in the clinic. Computational methodologies and analytical pipelines are key in facilitating scRNA-seq research. Numerous computational methods utilizing the most advanced tools in data science have been developed to extract meaningful insights. Here, we review the advancements in cancer biology gained by scRNA-seq and discuss the computational challenges of the technology that are specific to cancer research.

Gut microbiome is linked to functions of peripheral immune cells in transition cows during excessive lipolysis

BackgroundPostpartum dairy cows experiencing excessive lipolysis are prone to severe immunosuppression. Despite the extensive understanding of the gut microbial regulation of host immunity and metabolism, its role during excessive lipolysis in cows is largely unknown. Herein, we investigated the potential links between the gut microbiome and postpartum immunosuppression in periparturient dairy cows with excessive lipolysis using single immune cell transcriptome, 16S amplicon sequencing, metagenomics, and targeted metabolomics.ResultsThe use of single-cell RNA sequencing identified 26 clusters that were annotated to 10 different immune cell types. Enrichment of functions of these clusters revealed a downregulation of functions in immune cells isolated from a cow with excessive lipolysis compared to a cow with low/normal lipolysis. The results of metagenomic sequencing and targeted metabolome analysis together revealed that secondary bile acid (SBA) biosynthesis was significantly activated in the cows with excessive lipolysis. Moreover, the relative abundance of gut Bacteroides sp. OF04 − 15BH, Paraprevotella clara, Paraprevotella xylaniphila, and Treponema sp. JC4 was mainly associated with SBA synthesis. The use of an integrated analysis showed that the reduction of plasma glycolithocholic acid and taurolithocholic acid could contribute to the immunosuppression of monocytes (CD14+MON) during excessive lipolysis by decreasing the expression of GPBAR1.ConclusionsOur results suggest that alterations in the gut microbiota and their functions related to SBA synthesis suppressed the functions of monocytes during excessive lipolysis in transition dairy cows. Therefore, we concluded that altered microbial SBA synthesis during excessive lipolysis could lead to postpartum immunosuppression in transition cows.8-C9RL3NXjP8b3LTSGg7i4Video

Fibroblasts: Immunomodulatory factors in refractory diabetic wound healing.

Diabetes is a systemic disease in which patients with diabetes may develop peripheral neuropathy of the lower extremities and peripheral vascular disease due to long-term continuous exposure to high glucose. Delayed wound healing in diabetes is one of the major complications of diabetes. Slow wound healing in diabetic patients is associated with high glucose toxicity. When the condition deteriorates, the patient needs to be amputated, which seriously affects the quality of life and even endangers the life of the patient. In general, the delayed healing of diabetes wound is due to the lack of chemokines, abnormal inflammatory response, lack of angiogenesis and epithelial formation, and fibroblast dysfunction. The incidence of several chronic debilitating conditions is increasing in patients with diabetes, such as chronic renal insufficiency, heart failure, and hepatic insufficiency. Fibrosis is an inappropriate deposition of extracellular matrix (ECM) proteins. It is common in diabetic patients causing organ dysfunction. The fibrotic mechanism of diabetic fibroblasts may involve direct activation of permanent fibroblasts. It may also involve the degeneration of fibers after hyperglycemia stimulates immune cells, vascular cells, or organ-specific parenchymal cells. Numerous studies confirm that fibroblasts play an essential role in treating diabetes and its complications. The primary function of fibroblasts in wound healing is to construct and reshape the ECM. Nowadays, with the widespread use of single-cell RNA sequencing (scRNA-seq), an increasing number of studies have found that fibroblasts have become the critical immune sentinel cells, which can detect not only the activation and regulation of immune response but also the molecular pattern related to the injury. By exploring the heterogeneity and functional changes of fibroblasts in diabetes, the manuscript discusses that fibroblasts may be used as immunomodulatory factors in refractory diabetic wound healing, providing new ideas for the treatment of refractory diabetic wound healing.

AUTOMATED CELL TYPE IDENTIFICATION FOR SINGLE-CELL RNA SEQUENCING ANALYSIS OF WHOLE MOUSE KNEE JOINTS

Purpose: The use of single-cell RNA sequencing (scRNA-seq) is rapidly expanding and enables unprecedented insight into the role of tissue crosstalk in the pathogenesis and progression of osteoarthritis (OA). Our previous work describes a two-step digestion process to obtain a single cell suspension from all representative tissues in whole mouse knee joints, including cartilage, bone, fat, synovium, ligaments, tendons, meniscus, vasculature, epithelial, bone marrow, and immune cells. The whole knee digest creates a complex scRNA-seq dataset containing cells from clearly distinct tissues, many of which also have closely interrelated cell populations that require finer resolution.

Multiplexed droplet single-cell sequencing (Mux-Seq) of normal and transplant kidney

Maintenance of systemic homeostasis by kidney requires the coordinated response of diverse cell types. The use of single-cell RNA sequencing (scRNAseq) for patient tissue samples remains fraught with difficulties with cell isolation, purity, and experimental bias. The ability to characterize immune and parenchymal cells during transplant rejection will be invaluable in defining transplant pathology where tissue availability is restricted to needle biopsy fragments. Herein, we present feasibility data for multiplexing approach for droplet scRNAseq (Mux-Seq). Mux-Seq has the potential to minimize experimental batch bias and variation even with very small sample input. In this first proof-of-concept study for this approach, explant tissues from six normal and two transplant recipients after multiple early post-transplant rejection episodes leading to nephrectomy due to aggressive antibody mediated rejection, were pooled for Mux-Seq. A computational tool, Demuxlet was applied for demultiplexing the individual cells from the pooled experiment. Each sample was also applied individually in a single microfluidic run (singleplex) to correlate results with the pooled data from the same sample. Our applied protocol demonstrated that data from Mux-Seq correlated highly with singleplex (Pearson coefficient 0.982) sequencing results, with the ability to identify many known and novel kidney cell types including different infiltrating immune cells. Trajectory analysis of proximal tubule and endothelial cells demonstrated separation between healthy and injured kidney from transplant explant suggesting evolving stages of cell- specific differentiation in alloimmune injury. This study provides the technical groundwork for understanding the pathogenesis of alloimmune injury and host tissue response in transplant rejection and normal human kidney and provides a protocol for optimized processing precious and low input human kidney biopsy tissue for larger scale studies.

Dysregulation of interaction between LOXhigh fibroblast and smooth muscle cells contributes to the pathogenesis of aortic dissection.

Rationale: While cell-cell interaction plays a critical role in physiology and disease, a comprehensive understanding of its dynamics in vascular homeostasis and diseases is yet absent.Methods: Here, by use of single-cell RNA-sequencing and multi-color staining, we delineate the cellular composition and spatial characterization of human aorta with or without aortic dissection (AD).Results: Scrutinization of cell subtype alterations revealed significantly changed fibroblast (FB)-smooth muscle cell (SMC) interactions in AD. Of these cellular interactions, LOXhigh fibroblast (fibroblast subtype 2, FB2) in diseased state exerted the most pronounced effects on pathological deterioration of SMCs in AD. In addition, pharmacologically targeting the BMP (bone morphogenetic protein) signaling pathway effectively suppressed FB2 state transition and reduced AD incidence in mice. Finally, COL5A1 (collagen type V alpha 1 chain), one of the secreted proteins released from FB2, was significantly higher in the plasma of AD patients than in control patients, suggesting its potential use as a biomarker for AD diagnosis.Conclusions: Our work not only identified a pivotal role of a specific FB subtype in AD progression, but also shed light on cell interaction dynamics in vascular diseases.

Uncovering cell identity through differential stability with Cepo.

The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.

Implication of specific retinal cell-type involvement and gene expression changes in AMD progression using integrative analysis of single-cell and bulk RNA-seq profiling

Age‐related macular degeneration (AMD) is a blinding eye disease with no unifying theme for its etiology. We used single-cell RNA sequencing to analyze the transcriptomes of ~ 93,000 cells from the macula and peripheral retina from two adult human donors and bulk RNA sequencing from fifteen adult human donors with and without AMD. Analysis of our single-cell data identified 267 cell-type-specific genes. Comparison of macula and peripheral retinal regions found no cell-type differences but did identify 50 differentially expressed genes (DEGs) with about 1/3 expressed in cones. Integration of our single-cell data with bulk RNA sequencing data from normal and AMD donors showed compositional changes more pronounced in macula in rods, microglia, endothelium, Müller glia, and astrocytes in the transition from normal to advanced AMD. KEGG pathway analysis of our normal vs. advanced AMD eyes identified enrichment in complement and coagulation pathways, antigen presentation, tissue remodeling, and signaling pathways including PI3K-Akt, NOD-like, Toll-like, and Rap1. These results showcase the use of single-cell RNA sequencing to infer cell-type compositional and cell-type-specific gene expression changes in intact bulk tissue and provide a foundation for investigating molecular mechanisms of retinal disease that lead to new therapeutic targets.

Single-Cell RNA Sequencing Reveals Distinct Transcriptomic Profiles of Multiple Myeloma with Implications for Personalized Medicine

Multiple Myeloma (MM) is a heterogeneous malignancy with genomic translocations and mutations conferring varying levels of risk of recurrence and confounding treatment options across patients. Intra-tumor clonal heterogeneity has also been observed which further complicates treatment. We hypothesize that the use of Single-Cell RNA sequencing (scRNA-seq) can elucidate the complex clonal transcriptomic structure of individual patients' disease, and can be used in concert with other genomic techniques to better characterize and treat patients on a personalized level.Methods:Individual CD138+ tumor cells were isolated and barcoded from bone marrow aspirates from eight relapsed myeloma patients using the 10x Genomics Chromium microfluidics platform and V2 chemistry for deep sequencing using an Illumina HiSeq 2500 machine. The 10x Genomics Chromium platform allows for large-scale simultaneous cellular sequencing which facilitates a more robust analysis of cellular populations than previous technologies. The number of cells analyzed ranged from 393 to 3,359 per sample, depending on sample cell suspension density. We used 10x Genomics Loupe software to visualize scRNA-seq data via TSNE dimensional reduction plots. Cells were then clustered via Graph-Based, K-Means, or custom gating methods.Results:Analysis of bulk CD138+ whole-exome sequencing data has identified previously known mutational drivers and allowed estimation of cellular sub-populations (Fig. 1a) using copy-number alteration and mutational data via Phylo-WGS (Deshwar et al, Genome Biol 2015). Integrative analysis of bulk RNA-seq data revealed concordant activation of relevant pathways (e.g. MAP kinase) in patients with corresponding somatic mutations. On an individual patient level, we were able to process scRNA-seq data to visualize clusters of single cells with transcriptional similarity using t-distributed stochastic neighbor embedding (t-SNE) plots. We identified pathway activation in individual scRNA-seq clones via differential expression analysis and Signaling Pathway Impact Analysis (SPIA), in concordance with known somatic mutations (Fig. 1b). Genomic drivers and stages of differentiation in cell subsets (plasma cell vs immature B cell-like plasma cells) within a given patient were also revealed by distinct expression profiles (Fig. 1c). We utilized open-source drug repurposing tools with the RNA signature of individual clones to interrogate transcriptional signatures of drugs in public databases such as LINCS. The use of such drug repurposing methods will be effective in generating more targeted personalized therapies, utilizing cluster specific expression profiles. This is especially useful for cellular populations which do not contain a known target based on DNA mutational analysis. In a pooled analysis of all samples, each patient sample remarkably formed its own spatially distinct cluster of cells (Fig. 1d). These data suggest that relapsed myeloma patients form transcriptionally distinct clones, and myeloma therapy may need to be highly individualized to overcome clonal heterogeneity. scRNA-seq is a powerful tool which can be utilized alone, or in harmony with other sequencing technologies and genomic techniques, to improve characterization and treatment of myeloma patients in a clone specific manner. [Display omitted] DisclosuresMadduri:Foundation Medicine, Inc.: Consultancy. Chari:Novartis: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Millennium Pharmaceuticals, Inc.: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Amgen: Honoraria, Research Funding. Cho:Genentech: Other: advisory board, Research Funding; Bristol Myers-Squibb: Other: advisory board, Research Funding; Ludwig Institute for Cancer Research: Research Funding; Multiple Myeloma Research Foundation: Research Funding; Agenus, Inc.: Research Funding. Barlogie:Celgene Corporation: Consultancy, Research Funding; Millenium Pharmaceuticals: Consultancy, Research Funding. Jagannath:MMRF: Speakers Bureau; Medicom: Speakers Bureau; Merck: Consultancy; Novartis: Consultancy; Celgene: Consultancy; Bristol-Meyers Squibb: Consultancy. Dudley:NuMedii, Inc.: Equity Ownership, Patents & Royalties; Janssen Pharmaceuticals, Inc.: Consultancy; Ayasdi, Inc.: Equity Ownership; Ecoeos, Inc.: Equity Ownership; Ontomics, Inc.: Equity Ownership; Personalis: Patents & Royalties; AstraZeneca: Consultancy; GlaxoSmithKline: Consultancy.