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Recent advances in cancer immunotherapy have completely revolutionized cancer treatment strategies. Nonetheless, the increasing incidence of immune-related adverse events (irAEs) is now limiting the overall benefits of these treatments. irAEs are well-recognized side effects of some of the most effective cancer immunotherapy agents, including antibody blockade of the cytotoxic T-lymphocyte-associated protein 4 and programmed death protein 1/programmed-death ligand 1 pathways. To develop an action plan on the key elements needed to unravel and understand the key mechanisms driving irAEs, the Society for Immunotherapy for Cancer and the American Association for Cancer Research partnered to bring together research and clinical experts in cancer immunotherapy, autoimmunity, immune regulation, genetics and informatics who are investigating irAEs using animal models, clinical data and patient specimens to discuss current strategies and identify the critical next steps needed to create breakthroughs in our understanding of these toxicities. The genetic and environmental risk factors, immune cell subsets and other key immunological mediators and the unique clinical presentations of irAEs across the different organ systems were the foundation for identifying key opportunities and future directions described in this report. These include the pressing need for significantly improved preclinical model systems, broader collection of biospecimens with standardized collection and clinical annotation made available for research and integration of electronic health record and multiomic data with harmonized and standardized methods, definitions and terminologies to further our understanding of irAE pathogenesis. Based on these needs, this report makes a set of recommendations to advance our understanding of irAE mechanisms, which will be crucial to prevent their occurrence and improve their treatment.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.

Disease-causing variants in key immune homeostasis genes can lead to monogenic autoimmune diabetes. Some individuals carrying disease-causing variants do not develop autoimmune diabetes, even though they develop another autoimmune disease. We aimed to determine whether type 1 diabetes polygenic risk contributes to phenotypic presentation in monogenic autoimmune diabetes. We used a 67 single nucleotide polymorphism type 1 diabetes genetic risk score (T1D-GRS) to determine polygenic risk in 62 individuals with monogenic autoimmune diabetes and 180 control individuals with nonautoimmune neonatal diabetes (NDM). We used population-based control participants (n = 10,405) and individuals with type 1 diabetes (n = 285) as a comparator. Individuals with monogenic autoimmune diabetes had higher T1D-GRSs compared with individuals with nonautoimmune NDM (mean 11.3 vs. 9.8; P = 1.7 × 10−5) and controls (mean 10.3; P = 7.5 × 10−6), but the levels were markedly lower than those for individuals with type 1 diabetes (14.9; P = 3.3 × 10−21). These differences were explained by individuals with monogenic autoimmune diabetes having higher class II HLA genetic risk, specifically from the DRB1* 03:01 -DQA1 *05:01 -DQB1 *02:01 haplotype (DR3-DQ2) (P < 0.01). In the presence of monogenic autoimmunity, the polygenic class II HLA susceptibility contributes to development of autoimmune diabetes. This suggests a role of class II HLA in targeting the dysregulated immune response toward the β-cell. Article Highlights: There is variability in early-onset autoimmune diabetes presentation in individuals with monogenic autoimmunity; the mechanism(s) underlying this is unclear. We examined whether type 1 diabetes (T1D) polygenic risk contributes to clinical phenotype in monogenic autoimmune diabetes. Individuals with monogenic autoimmune diabetes had higher T1D genetic risk scores compared with control cohorts, driven largely by increased presence of T1D-risk DR3-DQ2 haplotype. Established T1D polygenic risk alleles, particularly class II HLA genes, contribute to clinical presentation in monogenic autoimmunity. [ABSTRACT FROM AUTHOR]

Background: Thymoglobulin is used to prevent allograft rejection and is being explored at low doses as intervention immunotherapy in type 1 diabetes. Thymoglobulin consists of a diverse pool of rabbit antibodies directed against many different targets on human thymocytes that can also be expressed by other leukocytes. Since Thymoglobulin is generated by injecting rabbits with human thymocytes, this conceivably leads to differences between Thymoglobulin batches. Methods: We compared different batches for antibody composition and variation between individuals in binding to PBMC and T cell subsets, and induction of cytokines. Four different batches of Thymoglobulin were directly conjugated with Alexa-Fluor 647. Blood was collected from five healthy donors, and PBMCs were isolated and stained with Thymoglobulin followed or preceded by a panel of fluorescent antibodies to identify PBMC and T cell subsets. In addition, whole blood was incubated with unlabeled Thymoglobulin to measure cytokine induction. Results: Cluster analysis of flow cytometry data shows that Thymoglobulin bound to all PBMC subpopulations including regulatory T cells. However, Thymoglobulin binding was highly variable between donors and to a lesser extent between batches. Cytokines related to cytokine release syndrome were highly, but variably, increased by all Thymoglobulin batches, with strong differences between donors and moderate differences between batches. Discussion: The variation in Thymoglobulin binding and action between donors regarding PBMC recognition and cytokine response may underlie the different clinical responses to Thymoglobulin therapy and require personalized dose adjustment to maximize efficacy and minimize adverse side effects. [ABSTRACT FROM AUTHOR]

Type 1 diabetes is an autoimmune disease arising from the destruction of pancreatic insulin-producing beta cells. The disease represents a continuum, progressing sequentially at variable rates through identifiable stages prior to the onset of symptoms, through diagnosis and into the critical periods that follow, culminating in a variable depth of beta cell depletion. The ability to identify the very earliest of these presymptomatic stages has provided a setting in which prevention strategies can be trialled, as well as furnishing an unprecedented opportunity to study disease evolution, including intrinsic and extrinsic initiators and drivers. This niche opportunity is occupied by Type 1 Diabetes TrialNet, an international consortium of clinical trial centres that leads the field in intervention and prevention studies, accompanied by deep longitudinal bio-sampling. In this review, we focus on discoveries arising from this unique bioresource, comprising more than 70,000 samples, and outline the processes and science that have led to new biomarkers and mechanistic insights, as well as identifying new challenges and opportunities. We conclude that via integration of clinical trials and mechanistic studies, drawing in clinicians and scientists and developing partnership with industry, TrialNet embodies an enviable and unique working model for understanding a disease that to date has no cure and for designing new therapeutic approaches.

Type 1 diabetes results from chronic autoimmune destruction of insulin-producing β-cells within pancreatic islets. Although insulin is a critical self-antigen in animal models of autoimmune diabetes, due to extremely limited access to pancreas samples, little is known about human antigenic targets for islet-infiltrating T cells. Here we show that proinsulin peptides are targeted by islet-infiltrating T cells from patients with type 1 diabetes. We identified hundreds of T cells from inflamed pancreatic islets of three young organ donors with type 1 diabetes with a short disease duration with high-risk HLA genes using a direct T-cell receptor (TCR) sequencing approach without long-term cell culture. Among 85 selected CD4 TCRs tested for reactivity to preproinsulin peptides presented by diabetes-susceptible HLA-DQ and HLA-DR molecules, one T cell recognized C-peptide amino acids 19-35, and two clones from separate donors responded to insulin B-chain amino acids 9-23 (B:9-23), which are known to be a critical self-antigen-driving disease progress in animal models of autoimmune diabetes. These B:9-23-specific T cells from islets responded to whole proinsulin and islets, whereas previously identified B:9-23 responsive clones from peripheral blood did not, highlighting the importance of proinsulin-specific T cells in the islet microenvironment.

Background/Objectives: Dendritic cells (DCs) are master regulators of the adaptive immune response. Inflammatory DCs (inflamDCs) can prime inflammatory T cells in, for instance, cancer and infection. In contrast, tolerogenic DCs (tolDCs) can suppress the immune system through a plethora of regulatory mechanisms in the context of autoimmunity. We successfully generated tolDCs in vitro to durably restore immune tolerance to an islet autoantigen in type 1 diabetes patients in a clinical trial. However, cancers can induce inhibitory DCs in vivo that impair anti-tumor immunity through Siglec signaling. Methods: To connect in vivo and in vitro tolDC properties, we tested whether tolDCs generated in vitro may also employ the Siglec pathway to regulate autoimmunity by comparing the transcriptomes and protein expression of immature and mature inflamDCs and tolDCs, generated from monocytes. Results: Both immature DC types expressed most Siglec genes. The expression of these genes declined significantly in mature inflamDCs compared to mature tolDCs. Surface expression of Siglec proteins by DCs followed the same pattern. The majority of genes involved in the different Siglec pathways were differentially expressed by mature tolDCs, as opposed to inflamDCs, and in inhibitory pathways in particular. Conclusions: Our results show that tolDCs generated in vitro mimic tumor-resident inhibitory DCs in vivo regarding Siglec expression. [ABSTRACT FROM AUTHOR]

OBJECTIVE Little is known about the influence of residual islet function on glycemic control in type 1 diabetes (T1D). We investigated the associations between residual β-cell function and metrics of continuous glucose monitoring (CGM) in individuals with T1D. RESEARCH DESIGN AND METHODS In this cross-sectional cohort comprising 489 individuals (64% female, age 41.0 ± 14.0 years), T1D duration was 15.0 (interquartile range [IQR] 6.0-29.0) years. Individuals had a time in range (TIR) of 66% (IQR 52-80%) and a urinary C-peptide-to-creatinine ratio (UCPCR) of 0.01 (IQR 0.00-0.41) nmol/mmol. To assess β-cell function, we measured UCPCR (detectable >0.01 nmol/mmol), and to assess α-cell function, fasting plasma glucagon/glucose ratios were measured. CGM was used to record TIR (3.9-10 mmol/L), time below range (TBR) (<3.9 mmol/L), time above range (TAR) (>10 mmol/L), and glucose coefficient of variance (CV). For CGM, 74.7% used FreeStyle Libre 2, 13.8% Medtronic Guardian, and 11.5% Dexcom G6 as their device. RESULTS The percentage of patients with T1D who had a detectable UCPCR was 49.4%. A higher UCPCR correlated with higher TIR (r = 0.330, P < 0.05), lower TBR (r = -0.237, P < 0.05), lower TAR (r = -0.302, P < 0.05), and lower glucose CV (r = -0.356, P < 0.05). A higher UCPCR correlated negatively with HbA1c levels (r = -0.183, P < 0.05) and total daily insulin dose (r = -0.183, P < 0.05). Glucagon/glucose ratios correlated with longer TIR (r = 0.234, P < 0.05). CONCLUSIONS Significantly longer TIR, shorter TBR and TAR, and lower CV were observed in individuals with greater UCPCR-assessed β-cell function. Therefore, better CGM-derived metrics in individuals with preserved β-cell function may be a contributor to a lower risk of developing long-term complications. [ABSTRACT FROM AUTHOR]

Regulatory T cells (Tregs) are essential to maintain immune homeostasis by promoting self-tolerance. Reduced Treg numbers or functionality can lead to a loss of tolerance, increasing the risk of developing autoimmune diseases. An overwhelming variety of human Tregs has been described, based on either specific phenotype, tissue compartment, or pathological condition, yet the bulk of the literature only addresses CD25-positive and CD127-negative cells, coined by naturally occurring Tregs (nTregs), most of which express the transcription factor Forkhead box protein 3 (FOXP3). While the discovery of FOXP3 was seminal to understanding the origin and biology of nTregs, there is evidence in humans that not all T cells expressing FOXP3 are regulatory, and that not all Tregs express FOXP3. Namely, the activation of human T cells induces the transient expression of FOXP3, irrespective of whether they are regulatory or inflammatory effectors, while some induced T cells that may be broadly defined as Tregs (e.g., Tr1 cells) typically lack demethylation and do not express FOXP3. Furthermore, it is unknown whether and how many nTregs exist without FOXP3 expression. Several other candidate regulatory molecules, such as GITR, Lag-3, GARP, GPA33, Helios, and Neuropilin, have been identified but subsequently discarded as Treg-specific markers. Multiparametric analyses have uncovered a plethora of Treg phenotypes, and neither single markers nor combinations thereof can define all and only Tregs. To date, only the functional capacity to inhibit immune responses defines a Treg and distinguishes Tregs from inflammatory T cells (Teffs) in humans. This review revisits current knowledge of the Treg universe with respect to their heterogeneity in phenotype and function. We propose that it is unavoidable to characterize human Tregs by their phenotype in combination with their function, since phenotype alone does not unambiguously define Tregs. There is an unmet need to align the expression of specific markers or combinations thereof with a particular suppressive function to coin functional Treg entities and categorize Treg diversity. [ABSTRACT FROM AUTHOR]

Aims/hypothesis The prevalence and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) in type 1 diabetes remain unclear. Therefore, we investigated the prevalence and severity of MASLD in type 1 diabetes and assessed which clinical features are most important in predicting MASLD severity. Methods A total of 453 individuals with type 1 diabetes (41.6 ± 15.0 years, 64% female, body mass index [BMI] 25.4 ± 4.2 kg/m2, and HbA1c 55.6 ± 12 mmol/mol) underwent vibration-controlled transient elastography (VCTE), with a controlled attenuation parameter (CAP) score for steatosis (≥ 280.0 dB/m) and a liver stiffness measurement (LMS) for fibrosis (≥ 8.0 kPa). A machine learning Extra-Trees classification model was performed to assess the predictive power of the clinical features associated with type 1 diabetes with respect to steatosis and fibrosis. Results The prevalence of hepatic steatosis and fibrosis was 9.5% (95% CI, 6.8-12.2) and 3.5% (95% CI, 1.8-5.2). Higher LMS was associated with a longer duration of type 1 diabetes (median 30.5 [IQR 18.0-39.3] years vs 15.0 [IQR 6.0-27.0] years), and individuals were older, had a higher BMI (mean 27.8 ± 5.2 vs 25.3 ± 4.1 kg/m2), and a higher CAP score (mean 211.4 ± 51.7 dB/m vs 241.4 ± 75.6 dB/m). The most important predictive features of fibrosis were duration of type 1 diabetes, age, and systolic blood pressure, with a mean ± SD area under the curve of 0.73 ± 0.03. Conclusion Individuals with type 1 diabetes and high blood pressure, older age, higher BMI, and longer duration of disease could be considered at high-risk for developing MASLD. [ABSTRACT FROM AUTHOR]

More than 30% of patients with type 1 diabetes develop diabetic kidney disease (DKD), which significantly increases mortality risk. The Diabetes Control and Complications Trial (DCCT) and follow-up study, Epidemiology of Diabetes Interventions and Complications (EDIC), established that glycemic control measured by HbA1c predicts DKD risk. However, the continued high incidence of DKD reinforces the urgent need for additional biomarkers to supplement HbA1c. Here, we assessed biomarkers induced bymethylglyoxal (MG), ametabolic by-product that forms covalent adducts on DNA, RNA, and proteins, called MG adducts. Urinary MG adducts were measured in samples from patients with type 1 diabetes enrolled in DCCT/EDIC who did (case patients; n = 90) or did not (control patients; n = 117) develop DKD. Univariate and multivariable analyses revealed that measurements of MG adducts independently predict DKD before established DKD biomarkers such as glomerular filtration rate and albumin excretion rate. Elevated levels of MG adducts bestowed the greatest risk of developing DKD in a multivariable model that included HbA1c and other clinical covariates. Our work establishes a novel class of biomarkers to predict DKD risk and suggests that inclusion of MG adducts may be a valuable tool to improve existing predictors of complications like DKD prior to overt disease, and to aid in identifying at-risk individuals and personalized risk management. [ABSTRACT FROM AUTHOR]