Lymphoid malignancies typically promote an infiltrate of immune cells at sites involved by the disease. While some of the immune cells present in lymphoma have effector function, the immune system is unable to eradicate the malignant clone. Therapies that optimize immune function therefore have the potential to improve the outcome of lymphoma patients. In this Review, we discuss immunologic approaches that directly target the malignant cell as well as approaches to optimize both the innate and adaptive immune response to the tumor. While many of these therapies have shown single-agent activity, the future will clearly require thoughtful combinations of these approaches.
Stephen M. Ansell, Yi Lin
Real-world data (RWD) continue to emerge as a new source of clinical evidence. Although the best-known use case of RWD has been in drug regulation, RWD are being generated and used by many other parties, including biopharmaceutical companies, payors, clinical researchers, providers, and patients. In this Review, we describe 21 potential uses for RWD across the spectrum of health care. We also discuss important challenges and limitations relevant to the translation of these data into evidence.
Vivek A. Rudrapatna, Atul J. Butte
Hematological malignancies have long been at the forefront of the development of novel immune-based treatment strategies. The earliest successful efforts originated from the extensive body of work in the field of allogeneic hematopoietic stem cell transplantation. These efforts laid the foundation for the recent exciting era of cancer immunotherapy, which includes immune checkpoint blockade, personal neoantigen vaccines, and adoptive T cell transfer. At the heart of the specificity of these novel strategies is the recognition of target antigens presented by malignant cells to T cells. Here, we review the advances in systematic identification of minor histocompatibility antigens and neoantigens arising from personal somatic alterations or recurrent driver mutations. These exciting efforts pave the path for the implementation of personalized combinatorial cancer therapy.
Livius Penter, Catherine J. Wu
Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.
Eugene Melamud, D. Leland Taylor, Anurag Sethi, Madeleine Cule, Anastasia Baryshnikova, Danish Saleheen, Nick van Bruggen, Garret A. FitzGerald
High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.
Sofia Khan, Gulayse Ince-Dunn, Anu Suomalainen, Laura L. Elo
Advanced phenotyping of cardiovascular diseases has evolved with the application of high-resolution omics screening to populations enrolled in large-scale observational and clinical trials. This strategy has revealed that considerable heterogeneity exists at the genotype, endophenotype, and clinical phenotype levels in cardiovascular diseases, a feature of the most common diseases that has not been elucidated by conventional reductionism. In this discussion, we address genomic context and (endo)phenotypic heterogeneity, and examine commonly encountered cardiovascular diseases to illustrate the genotypic underpinnings of (endo)phenotypic diversity. We highlight the existing challenges in cardiovascular disease genotyping and phenotyping that can be addressed by the integration of big data and interpreted using novel analytical methodologies (network analysis). Precision cardiovascular medicine will only be broadly applied to cardiovascular patients once this comprehensive data set is subjected to unique, integrative analytical strategies that accommodate molecular and clinical heterogeneity rather than ignore or reduce it.
Jane A. Leopold, Bradley A. Maron, Joseph Loscalzo
Over the past decade, great progress has been made in understanding the complexity of adipose tissue biology and its role in metabolism. This includes new insights into the multiple layers of adipose tissue heterogeneity, not only differences between white and brown adipocytes, but also differences in white adipose tissue at the depot level and even heterogeneity of white adipocytes within a single depot. These inter- and intra-depot differences in adipocytes are developmentally programmed and contribute to the wide range of effects observed in disorders with fat excess (overweight/obesity) or fat loss (lipodystrophy). Recent studies also highlight the underappreciated dynamic nature of adipose tissue, including potential to undergo rapid turnover and dedifferentiation and as a source of stem cells. Finally, we explore the rapidly expanding field of adipose tissue as an endocrine organ, and how adipose tissue communicates with other tissues to regulate systemic metabolism both centrally and peripherally through secretion of adipocyte-derived peptide hormones, inflammatory mediators, signaling lipids, and miRNAs packaged in exosomes. Together these attributes and complexities create a robust, multidimensional signaling network that is central to metabolic homeostasis.
C. Ronald Kahn, Guoxiao Wang, Kevin Y. Lee
The manner in which white adipose tissue (WAT) expands and remodels directly impacts the risk of developing metabolic syndrome in obesity. Preferential accumulation of visceral WAT is associated with increased risk for insulin resistance, whereas subcutaneous WAT expansion is protective. Moreover, pathologic WAT remodeling, typically characterized by adipocyte hypertrophy, chronic inflammation, and fibrosis, is associated with insulin resistance. Healthy WAT expansion, observed in the “metabolically healthy” obese, is generally associated with the presence of smaller and more numerous adipocytes, along with lower degrees of inflammation and fibrosis. Here, we highlight recent human and rodent studies that support the notion that the ability to recruit new fat cells through adipogenesis is a critical determinant of healthy adipose tissue distribution and remodeling in obesity. Furthermore, we discuss recent advances in our understanding of the identity of tissue-resident progenitor populations in WAT made possible through single-cell RNA sequencing analysis. A better understanding of adipose stem cell biology and adipogenesis may lead to novel strategies to uncouple obesity from metabolic disease.
Lavanya Vishvanath, Rana K. Gupta
The metabolic syndrome (MetS) is a constellation of risk factors that, if left untreated, will often progress to greater metabolic defects such as type 2 diabetes and nonalcoholic fatty liver disease. While these risk factors have been established for over 40 years, the definition of MetS warrants reconsideration in light of the substantial data that have emerged from studies of the gut microbiome. In this Review we present the existing recent literature that supports the gut microbiome’s potential influence on the various risk factors of MetS. The interplay of the intestinal microbiota with host metabolism has been shown to be mediated by a myriad of factors, including a defective gut barrier, bile acid metabolism, antibiotic use, and the pleiotropic effects of microbially produced metabolites. These data show that events that start in the gut, often in response to external cues such as diet and circadian disruption, have far-reaching effects beyond the gut.
Kruttika Dabke, Gustaf Hendrick, Suzanne Devkota
Although obesity is typically associated with metabolic dysfunction and cardiometabolic diseases, some people with obesity are protected from many of the adverse metabolic effects of excess body fat and are considered “metabolically healthy.” However, there is no universally accepted definition of metabolically healthy obesity (MHO). Most studies define MHO as having either 0, 1, or 2 metabolic syndrome components, whereas many others define MHO using the homeostasis model assessment of insulin resistance (HOMA-IR). Therefore, numerous people reported as having MHO are not metabolically healthy, but simply have fewer metabolic abnormalities than those with metabolically unhealthy obesity (MUO). Nonetheless, a small subset of people with obesity have a normal HOMA-IR and no metabolic syndrome components. The mechanism(s) responsible for the divergent effects of obesity on metabolic health is not clear, but studies conducted in rodent models suggest that differences in adipose tissue biology in response to weight gain can cause or prevent systemic metabolic dysfunction. In this article, we review the definition, stability over time, and clinical outcomes of MHO, and discuss the potential factors that could explain differences in metabolic health in people with MHO and MUO — specifically, modifiable lifestyle factors and adipose tissue biology. Better understanding of the factors that distinguish people with MHO and MUO can produce new insights into mechanism(s) responsible for obesity-related metabolic dysfunction and disease.
Gordon I. Smith, Bettina Mittendorfer, Samuel Klein
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