Sung, Anthony D.

Abstract Introduction Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20–30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq®, BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84–1.0) for identifying engraftment fevers with a mean AUROC of 0.88 ± 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning–based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment for many hematological malignancies, but graft-versus-host disease (GVHD) is a common complication. Low gut microbiome diversity is associated with higher GVHD risk and shorter survival in multiple studies. Recently, the BMT CTN 1703 clinical trial demonstrated superiority of a GVHD-prophylaxis regimen including post-transplant cyclophosphamide (PTCy) compared to the standard prophylaxis (tacrolimus and methotrexate, Tac/MTX) in terms of GVHD-free, relapse-free survival at one year among reduced intensity conditioning allo-HCT recipients. However, the effect of PTCy on the gut microbiome and its association with clinical outcome have not been described. Here, we report on a companion randomized clinical controlled trial (BMT CTN 1801), which collected 2575 longitudinal stool samples from 304 study participants. Samples were obtained up to weekly up to day 84 post allo-HCT and at less frequent intervals thereafter, up to 2 years. Microbiome diversity and absolute microbial load were lower in the PTCy group compared to the Tac/MTX group on days 14–28 post-HCT. However, diversity at the timepoint closest to neutrophil engraftment was not significantly associated with non-relapse mortality after one year or other clinical outcomes, contrary to expectations from previous studies. Microbial domination events, when a single species exceeds 30% relative abundance, were comparable across treatment arms and reflected both pathogen blooms as well as less severe disruptions of the microbial community. Clostridium scindens and secondary bile acid metabolism pathways were less prevalent in the PTCy arm than in the Tac/MTX arm post-HCT, yet presence of secondary bile acid metabolism pathways was associated with a lower risk of chronic GVHD. Given that PTCy was associated with a greater disruption of the microbiome as measured by diversity, absolute microbial abundance, and bile acid metabolism capability, but better clinical outcomes overall, these data suggest that the importance of the microbiome in modulating the host immune systems after allo-HCT is specific to different types of GVHD prophylaxis.

Abstract Background: A randomized multicenter trial (BMT CTN 1703) demonstrated that after reduced intensity conditioning HCT, PT-Cy-based GVHD prophylaxis led to significant reductions in acute and chronic GVHD, but was associated with significantly more Grade 2 (moderate) infections and similar overall survival vs Tac/MTX. A linked mechanistic study (BMT CTN 1801) found that PT-Cy led to pan-T cell depletion and reduced T cell receptor (TCR) repertoire diversity vs Tac/MTX. To discover the molecular mechanisms driving the association of PT-Cy with increased infections, we examined pathogen-specific T cell reconstitution in BMT CTN 1801 patients (n=165 PT-Cy, n=159 Tac/MTX). Methods: We performed deep TCR sequencing on 2,369 blood samples collected pre-HCT (recipient baseline) and on Days +7, 14, 28, 63, 100, 180, 270, 365, 730, and from the graft infusion product. We then applied a novel set of TCR classifiers (Adaptive Biotechnologies) capable of identifying pathogen-specific clones associated with CMV, EBV, SARS-CoV-2, HSV-1, HSV-2, parvovirus, RSV, influenza A, rhinovirus, adenovirus, HHV-6B, norovirus and Toxoplasma. Single-cell (sc) TCR/RNA-sequencing on a sample subset (n=26) was performed to determine the phenotype of pathogen-specific T cells. Results. By linking scRNA-seq with pathogen-specific TCR barcoding, we identified CD4 and CD8 memory T cells as their predominant T cell reservoir. We found that the total number of pathogen-specific singleton TCRs was significantly lower with PT-Cy, starting early post-HCT and persisting through 2 years. For example, at Day +14, PT-Cy patients had 7.8-fold fewer pathogen-specific singleton TCRs vs Tac/MTX (mean of 6.9 vs 54.1, p<0.001). Consistent differences between PT-Cy and Tac/MTX were observed for TCRs specific for each pathogen individually. Patients who developed Grade 3 (severe) viral infections had significantly fewer pathogen-specific singleton TCRs at Day +14 compared to those who had no viral infections (mean of 4.0 vs 32.5, p=0.03). Leveraging intrinsic TCR barcoding of the HCT infusion (“graft”) and of the patient's baseline samples (“recipient”), we could assign the graft/recipient origin of the pathogen-specific cells with high fidelity. This analysis revealed pronounced differences in the origin of pathogen-specific T cells with PT-Cy vs Tac/MTX, especially prior to Day +100. The absolute number of pathogen-specific TCRs originating from the graft was significantly higher with Tac/MTX vs PT-Cy. This difference was observed from day +7 through 6 months post-HCT, with up to 7.1-fold more graft-derived pathogen-specific TCRs in Tac/MTX vs PT-Cy (p<0.001 at each timepoint before day +100). Within patients who received Tac/MTX, the balance of graft vs recipient pathogen-specific clones significantly favored graft clones (for example, a mean of 14.9 graft vs 8.6 recipient pathogen-specific TCRs at Day+14, p=0.02). By contrast, with PT-Cy, in addition to having substantially fewer pathogen-specific TCRs overall, the proportion of these TCRs was not significantly different between graft and recipient clones (mean of 2.1 graft vs 3.3 recipient pathogen-specific TCRs at Day+14, p=0.42). These data uncover that, in addition to the reduction in pathogen-specific T cells with PT-Cy, there was a relative skewing of the anti-microbial T cell reservoir towards recipient cells that persisted after conditioning. Conclusions: PT-Cy led to in vivo T cell depletion across all T cell subsets, which prominently included clones tracked from the allograft itself into the HCT recipient, with a relative sparing of recipient T cell clones that survived transplant conditioning. While the mechanism for relative sparing of recipient cells has not yet been determined, these conditioning-exposed cells may be less proliferative than newly infused graft T cells. The depletion of graft T cells included CD4 and CD8 memory cells specific for infections, which may have contributed to relative deficits in protective immunity against infection in patients receiving PT-Cy. Importantly, these data suggest that with PT-Cy, persisting recipient TCRs may play a key role in preserving anti-infectious immunity. Taken together, these results provide a mechanistic basis for key post-transplant clinical outcomes of the landmark BMT CTN 1703 study. They also underscore the importance of recipient immune status with PT-Cy, creating the opportunity for targeted infection risk assessment in these patients.

Abstract Introduction: Graft Failure (GF) is a rare but devastating outcome of HCT. Primary GF (PGF), defined as a failure of neutrophil recovery by Day+28, is straightforward to diagnose. Diagnosing secondary GF (SGF) is more challenging, with its broad time-range, multiple confounding diagnoses, and lack of predictive biomarkers. In BMT CTN1703/1801, we analyzed patients receiving RIC HCT for heme malignancies with either Tac/MTX (n=159) or PT-Cy (n=165) GVHD prophylaxis. SGF was defined as donor chimerism <5% after initial donor engraftment. With Tac/MTX, there were 3 PGF and 1 SGF diagnoses. With PT-Cy, there were 4 PGF and 6 SGF. Median SGF diagnosis was Day +64 (range: Day+28-215). There were too few Tac/MTX patients to analyze SGF, but sufficient events with PT-Cy. To identify PT-Cy SGF predictors, we leveraged lymphocyte, T, B, and NK cell reconstitution analysis. In SGF we found an early, profound deficit in the reconstitution of all major lymphocyte populations, including total lymphocytes, T, B, and NK cells, as early as Day+28. This enabled the modeling of a SGF risk classifier based on the Absolute Lymphocyte Count (ALC). Methods: Clinical ALC measurements were performed on all SGF patients (n=6) and on non-GF controls with ALCs available (146 of 155 non-GF patients). Flow cytometry was performed on all PT-Cy SGF patients (n = 6) and from a subset of non-GF controls (n = 18: controls were chosen as patients without relapse or severe GVHD, to reduce confounders introduced by immunologic interventions, and for whom all samples, including from the graft infusion, were available). T, B, and NK counts, as well as T cell subsets, were compared (using Welch's T test) on Days 7, 14, 21, 28, 42, 63, 98, 180, 270, 365, 730, with SGF patients censored on the day of GF diagnosis. To interrogate the optimal ALC cutoff, the cumulative incidence of SGF was computed at Days +28 and +42, with death without GF as a competing risk. Results: We have previously demonstrated that, compared to Tac/MTX, PT-Cy patients exhibited an early decrease in reconstitution of all T cell populations (with normal NK and B cell reconstitution). Here we focused specifically on PT-Cy patients with or without SGF. Graft CD34 counts/kg were not different between SGF patients and non-GF patients (mean CD34/kg = 1.38 x10e6 (SGF) vs 0.67 x10e6 (non-GF, p = 0.61) However, even amidst the overarching early suppression in T cell reconstitution with PT-Cy, SGF patients could be easily distinguished from the larger PT-Cy cohort, based on more profound deficits in ALC, T, B and NK cells reconstitution, measured using 2 strategies: (1) SGF patients demonstrated significant early (Day +28) quantitative defects in the reconstitution of the ALC (mean +/- SEM 262+/-32 cells/µL (non-GF) vs 60 +/- 25 (SGF, p<0.0001), CD4 T cells (62 +/- 12 cells/µL vs 19+/-15, p=0.048), CD8 T cells (15 +/-3 cells/µL vs 3.5 +/-1 p=0.0009), all CD8 T cell subpopulations, as well as NK cells (116+/-31 cells/µL vs 2.9+/-1.4 cells/µL, p= 0.002) and B cells (3.5 +/-1.4 cells/µL vs 0.16+/-0.07 p =0.03). (2) In SGF, there was a significant deficit in the rate of rise of all major lymphocyte populations between Days 28-42-60 vs non-GF, including CD4 T cells (p<0.0001), CD8 T cells (p = 0.0002), B cells (p<0.0001), and NK cells (p =0.048). These discoveries suggested that a classifier could be identified to risk-stratify patients for SGF. We explored an ALC cutpoint, amenable to standard clinical lab analysis. A statistically significant threshold was identified at both Days+28 and +42, with Day+42 being most predictive: A threshold of 120 cells/µL was identified as optimal, with landmark analysis documenting a SGF rate of 34.7% below the cutpoint, and SGF of 1% above it (HR = 45.9, 95% CI 5.7 - 366). Conclusions: Despite the small number of events, PT-Cy patients with SGF demonstrated a distinctive reconstitution trajectory that encompassed an early, substantial, and sustained deficit in all lymphocyte counts, as well as a lack of their longitudinal expansion. This enabled the discovery of a candidate ALC biomarker cutpoint at Day+42 that could distinguish patients who were more likely to develop SGF. If confirmed, these data could generate a predictive biomarker for SGF, which would enable the design of trials evaluating early interventions (e.g. CD34+ boosts, DLI, modification of immunosuppression) to improve outcomes for these patients.

592 Background: While not all fevers occurring during hematopoietic stem cell transplantation (HCT) are dangerous, episodes of febrile neutropenia tend to be managed aggressively given the significant mortality and morbidity associated with infectious complications of transplantation. This can potentially lead to unnecessary diagnostic testing, antibiotic treatment, and prolonged hospital stays for non-infectious fever etiologies such as engraftment. Our group has previously demonstrated that high-frequency body temperature monitoring (HFTM) using a wearable device detects fevers earlier in transplant recipients (Flora et al, Cancer Cell, 2021). We hypothesized that applying supervised machine learning to HFTM data collected from inpatient allogeneic HCT patients would allow for accurately distinguishing between non-infectious and infectious etiologies of fever and could potentially guide safe de-escalation of antibiotics. Methods: We analyzed 74 independent fever events detected in HCT patients (n=42) outfitted with an FDA-cleared, wireless sensor measuring axillary temperatures every 2 minutes for the duration of their respective hospitalizations. After data filtering, independent fever events were then retrospectively grouped by fever etiology using physician-determined annotation as previously described (Flora et al., 2021). A split ratio of 80:20 was used to create training and test sets from the parent data set, consisting of 59 and 15 fever events for each split, respectively. Training sets were used to create a classification algorithm identifying bacterial infections requiring antibiotics using the machine learning model XGBoost. The performance of this model was then evaluated using test sets of the remaining fevers (n=15). Results: Averaging over 1000 test sets of 15 independent fevers comprising 5 bacterial infection-related fevers and 10 non-infectious fevers, our model correctly identified 82% (mean recall) of cases requiring antibiotics with a mean precision of 0.41. The model also correctly identified 42% of non-infectious fevers (mean true negative rate, 0.42) that would otherwise have been treated with unnecessary antibiotics. The mean AUCPR was 0.58, with the best performing individual model achieving an AUCPR of 0.97. Conclusions: The preliminary results of this study demonstrate that our classification model shows promise for differentiating fevers requiring antibiotics due to bacterial infections from other non-infectious fever etiologies. Prospective studies further developing supervised machine learning models for subcategorization of febrile neutropenia could pave the way for safely de-escalating antibiotics in HCT and other immunocompromised patients undergoing cancer treatment monitored using HFTM.

Abstract To examine the activity of ibrutinib in steroid-refractory chronic graft-versus-host disease (SR-cGVHD) after the US Food and Drug Administration approval, we conducted a multicenter retrospective study. Data were standardly collected (N = 270 from 19 centers). Involved organs included skin (75%), eye (61%), mouth (54%), joint/fascia (47%), gastrointestinal (GI) (26%), lung (27%), liver (19%), genital (7%), and others (4.4%). The National Institutes of Health (NIH) severity was mild in 5.7%, moderate 42%, and severe 53%. Thirty-nine percent had overlap subtype. Karnofsky performance status (KPS) was ≥80% in 72%. The median prednisone was 0.21 mg/kg (0-2.27). Ibrutinib was started at a median of 18.2 months after cGVHD onset and in earlier lines of therapy (second line, 26%; third, 30%; fourth, 21%; fifth, 9.6%; sixth, 10%; seventh or higher, 1.2%). Among evaluable patients, the 6-month NIH overall response rate (ORR; complete response [CR]/partial response [PR]) was 45% (PR 42%; CR 3%). The median duration of response was 15 months (range, 1-46). Liver involvement had association with 6-month ORR (multivariate [MVA] odds ratio, 5.49; 95% confidence interval [CI], 2.3-14.2; P < .001). The best overall response was 56%, with most (86%) achieving by 1 to 3 months. With a median follow-up for survivors of 30.5 months, failure-free survival (FFS) was 59% (53%-65%) at 6 months and 41% (36%-48%) at 12 months. On MVA, increased age (hazard ratio [HR], 1.01; 95% CI, 1.0-1.02; P = .033), higher baseline prednisone (HR, 1.92; 95% CI, 1.09-3.38; P = .032), and lung involvement (HR, 1.58; 95% CI, 1.1-2.28; P = .016) had worse FFS. Ibrutinib discontinuation was most commonly due to progressive cGVHD (44%) or toxicity (42%). These data support that ibrutinib has activity in SR-cGVHD, provide new insight into factors associated with response and FFS, and demonstrate the toxicity profile associated with discontinuation.

Background: BMT CTN 1703 was a Phase 3 trial comparing Tac/MTX (n = 217) to post-transplant cyclophosphamide (PT-Cy)/Tac/MMF (n = 214) GVHD prophylaxis after reduced intensity conditioning unrelated donor hematopoietic cell transplant (HCT). PT-Cy significantly improved GVHD-free Relapse-Free Survival (Bolanos-Meade et al NEJM 2023). However overall survival was not significantly different and the # of Grade 2 infections was higher with PT-Cy. To investigate its biologic underpinnings 1703 was linked to a mechanistic study, BMT CTN 1801, which co-enrolled 324 pts (159 CNI/MTX 165 PT-Cy). The resulting dataset enabled an analysis of post-HCT T cell reconstitution at an unprecedented level of detail. Methods: Samples were collected from the HCT infusion and on D+7, 14, 28, 63, 98, 180, 270, 1- and 2-yr. We extracted DNA (2,225 samples) and performed Adaptive Biotech β-strand T Cell Receptor (TCR) Immunosequencing (TCR-Seq). We profiled 44,077 (median) TCRs/sample resulting in 215,155,719 T cells analyzed. We evaluated multiple TCR diversity measures longitudinally and before/after cGVHD and infections. Each measure evaluates a specific aspect of TCR diversity, including Simpsons Clonality (focuses on the most expanded clones) Diversity Slope (mid-range clones) Richness (both mid-range and less-expanded clones) and Singletons (the TCR-Seq equivalent of naïve T cells). This analysis was robust to the full range (1000-50,000) of down-sampled TCRs sequenced. Results: We identified major differences in TCR reconstitution in Tac/MTX vs PT-Cy that begin early and persist through 2 yr post-HCT. These resulted in a significantly less diverse TCR repertoire with PT-Cy with substantial implications for its efficacy and toxicity. The most salient differences are: (1) PT-Cy caused significant in-vivo T cell depletion early post-HCT (% T cells/total WBC at D+28: 8.2% (Tac/MTX) vs 2.2% (PT-Cy) p=2.2x10 -23). (2) The remaining donor T cells in PT-Cy undergo substantial clonal expansion ( Fig 1) resulting in significantly constrained TCR diversity measured by Clonality, Slope, Richness, Singletons (not shown) and Singletons/Richness ( Fig 2) which began early (D+14) and persisted for 2 yr. (3) Linked T cell depletion and clonal expansion resulted in a TCR diversity bottleneck after PT-Cy ( Fig 1). Due to the bottleneck, while the fraction of T cells originating from the transplant infusion at =/> 1 yr was similar between arms (18% vs 24%, p=0.7) there were significantly fewer Singleton TCRs in PT-Cy, suggesting a reduction in naïve T cells ( Fig 2), an outcome that has critical consequences for both cGVHD and infection. Thus while cGVHD onset with Tac/MTX was linked to a significant drop in TCR diversity due to singleton clonal expansion (median log-2 fold change in singletons with Tac/MTX = -0.25 p = 0.01) this drop was not observed with PT-Cy (log-2 fold change = -0.05, p = 0.1). Along with the significantly decreased # singletons at all time-points in PT-Cy ( Fig 2) this suggests that while cGVHD after Tac/MTX is linked to substantial naïve T cell expansion, the same is not true with PT-Cy, identifying a novel mechanism for cGVHD control with PT-Cy. The substantially fewer singletons in PT-Cy vs Tac/MTX may also increase the risk of Grade 2+ infections. T cell responses to new infections rely on antigen recognition/effector maturation of naïve (singleton TCR) T cells. After infection, the TCR repertoire became less diverse for both Tac/MTX and PT-Cy (log-2-fold singleton change -0.17 and -1.2 p = 0.0006 and 0.01) indicating that singletons in both cohorts were capable of expanding to pathogens. However the markedly decreased # singletons in PT-Cy at all timepoints was associated with significantly fewer singletons prior to infection (mean 3979 at 5K read-depth for Tac/MTX vs 2820 for PT-Cy p<0.001). This singleton deficit would place PT-Cy patients at higher risk of missing the critical mass of TCRs required to respond effectively to infections, consistent with their increased rate of Gr 2 infections. Conclusions: We performed longitudinal TCR-Seq on >200 million T cells from 324 HCT patients, representing the largest post-HCT TCR-Seq analysis in the history of the field, and enabling novel insights into TCR dynamics. These data identify a previously unappreciated global TCR diversity bottleneck with PT-Cy that simultaneously protects patients from cGVHD and places them at higher risk of infectious complications.

Abstract Introduction: The gut microbiome is a potentially modifiable factor in treatment related outcomes in allogeneic hematopoietic cell transplant (HCT). Prior studies have linked pre- or mid-treatment gut microbiome diversity with risk for treatment related morbidity and mortality. However, these studies have been limited by the inclusion of one or only a few institutions and the lack of longitudinal sampling with high quality metadata. These limitations complicate the interpretation of microbiome alterations over the course of HCT. Methods: To overcome these, we devised and implemented a large-scale biospecimen collection protocol in conjunction with BMT CTN 1703, a randomized, multicenter, Phase III trial of tacrolimus/methotrexate vs. post-transplant cyclophosphamide/tacrolimus/mycophenolate mofetil in reduced intensity conditioning (RIC) allogeneic HCT (NCT03959241). Patients enrolled on 1703 were optionally co-enrolled in the companion immune and microbiome profiling study, 1801 (MI-IMMUNE). This involved blood, urine and stool sampling before conditioning (PCON), then weekly starting on day 0 through day 77 (through day 84 for blood), and on days 98, 180, 270, 365 and 730. For all enrolled participants where the donor consented, residual donor cells were saved from the empty hematopoietic stem cell product bag for later analysis. Additionally, the protocol included one-time blood, urine and stool sample collection from consented matched related donors (MRDs) prior to stem cell collection. Starting with protocol version 4.0 on February 1, 2021, participation in 1801 stool collection was required for the first six out of eighteen sample collection timepoints. Participation in later stool and urine timepoint collections remained optional. Here we review the feasibility of creating a multi-institutional biobank. Additionally, we assess the success of our strategies by calculating sample collection compliance and standard deviation in compliance across centers for each timepoint. Results: On June 18, 2021, BMT CTN 1703/1801 closed to accrual with 431 patients enrolled on 1703; 323 patients from 36 centers were co-enrolled on 1801. 304 (94%) provided study samples, making this the largest prospective microbiome and immune profiling study in HCT patients to date. As of July 6, 2021, 3,683 blood, 2,668 urine, and 2,098 stool samples had been collected. Across the first 6 timepoints for all participating centers, blood, urine and stool sample collection averaged 93%, 82%, and 74% compliance, respectively. Of the 99 (30%) patients enrolled on 1801 with a MRD, 34 (34%) donors consented to sample collection. Sample collection compliance was lower for MRDs than for patients on the study with 76%, 74%, and 62% of expected blood, urine and stool samples collected, respectively, from this group. For stool collection exclusively, a median of 5 samples were collected per patient across the first 6 timepoints (median of 6 across all timepoints) with 93 (31%) of patients completing a full sample set through Day 28. 139 (46%) patients provided at least one sample after day 28; these represented 37% of the total samples collected to date. The PCON sample, which provides an important measure of pre-treatment gut microbiome diversity, had the third highest compliance with 74% of patients providing a sample. Surprisingly, Day 28 had the lowest compliance (66%) and highest standard deviation (37%) possibly because this timepoint often falls around the time of hospital discharge. Between PCON and day 28, the standard deviation between sites in the average collection compliance (24%) and number of samples collected per patient (1.1) was small indicating the successful adoption of stool collection across institutions. Table 1 summarizes sample collection statistics. Conclusion: Overall this study has resulted in a large, novel biobank of blood, urine and stool samples from patients undergoing RIC allogeneic HCT at 36 centers across the US. This will serve as a valuable resource for investigating the role of the gut microbiome in long term health outcomes following HCT. Although the results of 1801 are forthcoming given ongoing sample collection, the size and composition of the biobank to date clearly demonstrate the feasibility of implementing multi-institutional stool collection. This study represents a critical step towards the large-scale adoption of microbiome sampling as a diagnostic tool. Figure 1 Figure 1. Disclosures Chhabra: GSK: Honoraria. Clark: Kadmon: Consultancy. Horowitz: Mesoblast: Research Funding; Shire: Research Funding; Vertex: Research Funding; Stemcyte: Research Funding; Vor Biopharma: Research Funding; Janssen: Research Funding; Miltenyi Biotech: Research Funding; Kiadis: Research Funding; Sobi: Research Funding; Kite/Gilead: Research Funding; Pfizer, Inc: Research Funding; Jazz Pharmaceuticals: Research Funding; Magenta: Consultancy, Research Funding; Medac: Research Funding; Novartis: Research Funding; GlaxoSmithKline: Research Funding; Daiicho Sankyo: Research Funding; Xenikos: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Tscan: Research Funding; Takeda: Research Funding; CSL Behring: Research Funding; Genentech: Research Funding; Gamida Cell: Research Funding; Chimerix: Research Funding; Bristol-Myers Squibb: Research Funding; bluebird bio: Research Funding; Astellas: Research Funding; Amgen: Research Funding; Allovir: Consultancy; Actinium: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding. Jenq: Microbiome DX: Consultancy; Merck: Consultancy; Prolacta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kaleido: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seres: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; LisCure: Consultancy, Membership on an entity's Board of Directors or advisory committees; MaaT Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karius: Consultancy. Levine: Equillium Bio: Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Research Funding; Kamada: Research Funding; Biogen: Research Funding; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Murthy: CRISPR Therapeutics: Research Funding. Riches: ATARA Biotherapeutics: Other: Payment; BioIntelect: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Other: Payment. Sung: Merck: Research Funding; Novartis: Research Funding; Enterome: Research Funding; Seres: Research Funding; AVROBIO: Consultancy; Abbott Nutrition: Honoraria; Clasado: Other: Research Product; DSM: Other: Research Product. Al Malki: Neximmune: Consultancy; Jazz Pharmaceuticals, Inc.: Consultancy; Rigel Pharma: Consultancy; Hansa Biopharma: Consultancy; CareDx: Consultancy. Rezvani: Kaleido: Other: One-time scientific advisory board; Nohla Therapeutics: Other: One-time scientific advisory board; Pharmacyclics-Abbvie: Research Funding; US Department of Justice: Consultancy. Bolaños-Meade: Incyte Corp: Consultancy. Holtan: Incyte: Consultancy, Research Funding; Generon: Consultancy. Saber: Govt. COI: Other. Hamadani: Sanofi, Genzyme, AstraZeneca, BeiGene: Speakers Bureau; Janssen, Incyte, ADC Therapeutics, Omeros, Morphosys, Kite: Consultancy; Takeda, Spectrum Pharmaceuticals and Astellas Pharma: Research Funding. Kean: Bluebird Bio: Research Funding; Bristol Myers Squibb: Patents & Royalties: From clinical trial data, Research Funding; Vertex: Consultancy; Novartis: Consultancy; Gilead: Research Funding; Regeneron: Research Funding; EMD Serono: Consultancy. Perales: Cidara: Honoraria; Servier: Honoraria; Incyte: Honoraria, Other; Equilium: Honoraria; Takeda: Honoraria; Novartis: Honoraria, Other; Nektar Therapeutics: Honoraria, Other; NexImmune: Honoraria; MorphoSys: Honoraria; Omeros: Honoraria; Karyopharm: Honoraria; Sellas Life Sciences: Honoraria; Merck: Honoraria; Miltenyi Biotec: Honoraria, Other; Medigene: Honoraria; Bristol-Myers Squibb: Honoraria; Kite/Gilead: Honoraria, Other; Celgene: Honoraria.

The Internet of Things (IoT) has penetrated many aspects of everyday human life. The use of IoT in healthcare has been expanding over the past few years. In this review, we highlighted the current applications of IoT in the medical literature, along with the challenges and opportunities. IoT use mainly involves sensors and wearables, with potential applications in improving the quality of life, personal health monitoring, and diagnosis of diseases. Our literature review highlights that the current main application studied in the literature is physical activity tracking. In addition, we discuss the current technologies that would help IoT-enabled devices achieve safe, quick, and meaningful data transfer. These technologies include machine learning/artificial intelligence, 5G, and blockchain. Data on current IoT-enabled devices are still limited, and future research should address these devices’ effect on patients’ outcomes and the methods by which their integration in healthcare will avoid increasing costs.