Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques-including statistical, regularization, deep learning, decision trees, and Bayesian methods-were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.

Multi-output prediction of dose-response curves enables drug repositioning and biomarker discovery

Drug response prediction is hampered by uncertainty in the measures of response and selection of doses. In this study, we propose a probabilistic multi-output model to simultaneously predict all dose-responses and uncover their biomarkers. By describing the relationship between genomic features and chemical properties to every response at every dose, our multi-output Gaussian Process (MOGP) models enable assessment of drug efficacy using any dose-response metric. This approach was tested across two drug screening studies and ten cancer types. Kullback-leibler divergence measured the importance of each feature and identified EZH2 gene as a novel biomarker of BRAF inhibitor response. We demonstrate the effectiveness of our MOGP models in accurately predicting dose-responses in different cancer types and when there is a limited number of drug screening experiments for training. Our findings highlight the potential of MOGP models in enhancing drug development pipelines by reducing data requirements and improving precision in dose-response predictions.

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Drug targets are specific molecules in biological tissues and body fluids that interact with drugs. Drug target discovery is a key component of drug discovery and is essential for the development of new drugs in areas such as cancer therapy and precision medicine. Traditional in vitro or in vivo target discovery methods are time-consuming and labor-intensive, limiting the pace of drug discovery. With the development of modern discovery methods, the discovery and application of various emerging technologies have greatly improved the efficiency of drug discovery, shortened the cycle time, and reduced the cost. This review provides a comprehensive overview of various emerging drug target discovery strategies, including computer-assisted approaches, drug affinity response target stability, multiomics analysis, gene editing, and nonsense-mediated mRNA degradation, and discusses the effectiveness and limitations of the various approaches, as well as their application in real cases. Through the review of the aforementioned contents, a general overview of the development of novel drug targets and disease treatment strategies will be provided, and a theoretical basis will be provided for those who are engaged in pharmaceutical science research. SIGNIFICANCE STATEMENT: Target-based drug discovery has been the main approach to drug discovery in the pharmaceutical industry for the past three decades. Traditional drug target discovery methods based on in vivo or in vitro validation are time-consuming and costly, greatly limiting the development of new drugs. Therefore, the development and selection of new methods in the drug target discovery process is crucial.

From bytes to nephrons: AI's journey in diabetic kidney disease

Diabetic kidney disease (DKD) is a significant complication of type 2 diabetes, posing a global health risk. Detecting and predicting diabetic kidney disease at an early stage is crucial for timely interventions and improved patient outcomes. Artificial intelligence (AI) has demonstrated promise in healthcare, and several tools have recently been developed that utilize Machine Learning with clinical data to detect and predict DKD. This review aims to explore the current landscape of AI and machine learning applications in DKD, specifically examining existing literature on risk scores and machine learning approaches for predicting DKD development. A literature search was conducted using Medline (PubMed), Google Scholar, and Scopus databases until July 2023. Relevant keywords were used to extract studies that described the role of AI in DKD. The review revealed that AI and machine learning have been successfully used to predict DKD progression, outperforming traditional risk score models. Artificial intelligence-driven research for DKD extends beyond prediction models, offering opportunities for integrating genetic and epigenetic data, advancing understanding of the disease's molecular basis, personalizing treatment strategies, and fostering the development of novel drugs. However, challenges remain, including the requirement for large datasets and the lack of standardization in AI-driven tools for DKD. Artificial intelligence and machine learning have the potential to revolutionize the management and care of DKD patients, surpassing the limitations of traditional methods reliant on existing knowledge. Future research should address the challenges associated with AI and machine learning in DKD and focus on developing AI-driven tools for clinical practice.

Pathology of pain and its implications for therapeutic interventions

Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.

An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia

BACKGROUND

The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia.

METHODS

We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively.

FINDINGS

By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups.

INTERPRETATION

An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries.

FUNDING

German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

Insights into multilevel spatial regulation within the root stem cell niche

All differentiated root cells derive from stem cells spatially organized within the stem cell niche (SCN), a microenvironment located within the root tip. Here, we compiled recent advances in the understanding of how the SCN drives the establishment and maintenance of cell types. The quiescent center (QC) is widely recognized as the primary driver of cell fate determination, but it is recently considered a convergence center of multiple signals. Cell identity of the cortex endodermis initials is mainly driven by the regulatory feedback loops between transcription factors (TFs), acting as mobile signals between neighboring cells, including the QC. As exemplified in the vascular initials, the precise spatial expression of these regulatory TFs is connected with a dynamic hormonal interplay. Thus, stem cell maintenance and cell differentiation are regulated by a plethora of signals forming a complex, multilevel regulatory network. Integrating the transcriptional and post-translational regulations, protein-protein interactions, and mobile signals into models will be fundamental for the comprehensive understanding of SCN maintenance and differentiation.

Question-based computational language approach outperforms rating scales in quantifying emotional states

Psychological constructs are commonly quantified with closed-ended rating scales. However, recent advancements in natural language processing (NLP) enable the quantification of open-ended language responses. Here we demonstrate that descriptive word responses analyzed using NLP show higher accuracy in categorizing emotional states compared to traditional rating scales. One group of participants (N = 297) generated narratives related to depression, anxiety, satisfaction, or harmony, summarized them with five descriptive words, and rated them using rating scales. Another group (N = 434) evaluated these narratives (with descriptive words and rating scales) from the author's perspective. The descriptive words were quantified using NLP, and machine learning was used to categorize the responses into the corresponding emotional states. The results showed a significantly higher number of accurate categorizations of the narratives based on descriptive words (64%) than on rating scales (44%), questioning the notion that rating scales are more precise in measuring emotional states than language-based measures.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Nanomedomics

Nanomaterials have attractive physicochemical properties. A variety of nanomaterials such as inorganic, lipid, polymers, and protein nanoparticles have been widely developed for nanomedicine via chemical conjugation or physical encapsulation of bioactive molecules. Superior to traditional drugs, nanomedicines offer high biocompatibility, good water solubility, long blood circulation times, and tumor-targeting properties. Capitalizing on this, several nanoformulations have already been clinically approved and many others are currently being studied in clinical trials. Despite their undoubtful success, the molecular mechanism of action of the vast majority of nanomedicines remains poorly understood. To tackle this limitation, herein, this review critically discusses the strategy of applying multiomics analysis to study the mechanism of action of nanomedicines, named nanomedomics, including advantages, applications, and future directions. A comprehensive understanding of the molecular mechanism could provide valuable insight and therefore foster the development and clinical translation of nanomedicines.

Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials

The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for developing data-centric materials science, such as those using graph neural networks (GNNs). However, the currently prevalent GNN models are primarily employed for investigating periodic crystals and organic small molecule data, yet they still encounter challenges in terms of interpretability and computational efficiency when applied to polymer monomers and organic macromolecules data. There is still a lack of graph representation of organic polymers and macromolecules specifically tailored for GNN models to explore the structural characteristics. The Polymer-unit Graph, a novel coarse-grained graph representation method introduced in study, is dedicated to expressing and analyzing polymers and macromolecules. By incorporating the Polymer-unit Graph into the GNN models and analyzing the organic semiconductor (OSC) materials database, it becomes possible to uncover intricate structure-property relationships involving branched-chain engineering, fluoridation substitution, and donor-acceptor combination effects on the elementary structure of OSC polymers. Furthermore, the Polymer-unit Graph enables visualizing the relationship between target properties and polymer units while reducing training time by an impressive 98% and minimizing molecular graph representation models. In conclusion, the Polymer-unit Graph successfully integrates the concept of Polymer-unit into the field of GNNs, enabling more accurate analysis and understanding of organic polymers and macromolecules.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Novel Techniques and Models for Studying the Role of the Gut Microbiota in Drug Metabolism

The gut microbiota, known as the second human genome, plays a vital role in modulating drug metabolism, significantly impacting therapeutic outcomes and adverse effects. Emerging research has elucidated that the microbiota mediates a range of modifications of drugs, leading to their activation, inactivation, or even toxication. In diverse individuals, variations in the gut microbiota can result in differences in microbe-drug interactions, underscoring the importance of personalized approaches in pharmacotherapy. However, previous studies on drug metabolism in the gut microbiota have been hampered by technical limitations. Nowadays, advances in biotechnological tools, such as microbially derived metabolism screening and microbial gene editing, have provided a deeper insight into the mechanism of drug metabolism by gut microbiota, moving us toward personalized therapeutic interventions. Given this situation, our review summarizes recent advances in the study of gut-microbiota-mediated drug metabolism and showcases techniques and models developed to navigate the challenges posed by the microbial involvement in drug action. Therefore, we not only aim at understanding the complex interaction between the gut microbiota and drugs and outline the development of research techniques and models, but we also summarize the specific applications of new techniques and models in researching gut-microbiota-mediated drug metabolism, with the expectation of providing new insights on how to study drug metabolism by gut microbiota.

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.

Exploring noncoding variants in genetic diseases: from detection to functional insights

Previous studies on genetic diseases predominantly focused on protein-coding variations, overlooking the vast noncoding regions in the human genome. The development of high-throughput sequencing technologies and functional genomics tools has enabled the systematic identification of functional noncoding variants. These variants can impact gene expression, regulation, and chromatin conformation, thereby contributing to disease pathogenesis. Understanding the mechanisms that underlie the impact of noncoding variants on genetic diseases is indispensable for the development of precisely targeted therapies and the implementation of personalized medicine strategies. The intricacies of noncoding regions introduce a multitude of challenges and research opportunities. In this review, we introduce a spectrum of noncoding variants involved in genetic diseases, along with research strategies and advanced technologies for their precise identification and in-depth understanding of the complexity of the noncoding genome. We will delve into the research challenges and propose potential solutions for unraveling the genetic basis of rare and complex diseases.

Drug and gut microbe relationships: Moving beyond antibiotics

Our understanding of drug-microbe relationships has evolved from viewing microbes as mere drug producers to a dynamic, modifiable system where they can serve as drugs or targets of precision pharmacology. This review highlights recent findings on the gut microbiome, particularly focusing on four aspects of research: (i) drugs for bugs, covering recent strategies for targeting gut pathogens; (ii) bugs as drugs, including probiotics; (iii) drugs from bugs, including postbiotics; and (iv) bugs and drugs, discussing additional types of drug-microbe interactions. This review provides a perspective on future translational research, including efficient companion diagnostics in pharmaceutical interventions.

Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers

Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Contrastively generative self-expression model for single-cell and spatial multimodal data

Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-expression relationship to consider the characteristics of different omics data by deep self-expressive generative model. Then, scMSI combines these omics-specific self-expression relations through contrastive learning. In such a way, scMSI provides a paradigm to integrate multiple omics data even with weak relation, which effectively achieves the representation learning and data integration into a unified framework. We demonstrate that scMSI provides a cohesive solution for a variety of analysis tasks, such as integration analysis, data denoising, batch correction and spatial domain detection. We have applied scMSI on various single-cell and spatial multimodal datasets to validate its high effectiveness and robustness in diverse data types and application scenarios.

An Overview of Deep Generative Models in Functional and Evolutionary Genomics

Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.

Type 2 Diabetes Mellitus and Efficacy Outcomes from Immune Checkpoint Blockade in Patients with Cancer

PURPOSE

No evidence exists as to whether type 2 diabetes mellitus (T2DM) impairs clinical outcome from immune checkpoint inhibitors (ICI) in patients with solid tumors.

EXPERIMENTAL DESIGN

In a large cohort of ICI recipients treated at 21 institutions from June 2014 to June 2020, we studied whether patients on glucose-lowering medications (GLM) for T2DM had shorter overall survival (OS) and progression-free survival (PFS). We used targeted transcriptomics in a subset of patients to explore differences in the tumor microenvironment (TME) of patients with or without diabetes.

RESULTS

A total of 1,395 patients were included. Primary tumors included non-small cell lung cancer (NSCLC; 54.7%), melanoma (24.7%), renal cell (15.0%), and other carcinomas (5.6%). After multivariable analysis, patients on GLM (n = 226, 16.2%) displayed an increased risk of death [HR, 1.29; 95% confidence interval (CI),1.07-1.56] and disease progression/death (HR, 1.21; 95% CI, 1.03-1.43) independent of number of GLM received. We matched 92 metformin-exposed patients with 363 controls and 78 patients on other oral GLM or insulin with 299 control patients. Exposure to metformin, but not other GLM, was associated with an increased risk of death (HR, 1.53; 95% CI, 1.16-2.03) and disease progression/death (HR, 1.34; 95% CI, 1.04-1.72). Patients with T2DM with higher pretreatment glycemia had higher neutrophil-to-lymphocyte ratio (P = 0.04), while exploratory tumoral transcriptomic profiling in a subset of patients (n = 22) revealed differential regulation of innate and adaptive immune pathways in patients with T2DM.

CONCLUSIONS

In this study, patients on GLM experienced worse outcomes from immunotherapy, independent of baseline features. Prospective studies are warranted to clarify the relative impact of metformin over a preexisting diagnosis of T2DM in influencing poorer outcomes in this population.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.