Citations

Thalamic neuromodulation for drug-resistant epilepsy has evolved from ablative thalamotomy to network-guided deep-brain stimulation (DBS) and responsive neurostimulation (RNS). Currently, the only Food and Drug Administration-approved DBS target is the anterior thalamic nucleus, which provides substantial and durable seizure reduction, particularly in limbic and temporal epilepsies. There is growing evidence that the centromedian and pulvinar nuclei are implicated in generalized, multifocal, and posterior neocortical epilepsies. Advanced imaging, probabilistic atlases, and connectomics can be used to refine targeting; however, they are limited by patient specificity, acquisition burden, and interatlas variability. Thalamic stereoelectroencephalography may clarify potential neuromodulation target selection by characterizing ictal recruitment, network dynamics, and biomarkers. Although DBS and RNS each have unique benefits, no modality has demonstrated superior. Pediatric data are heterogeneous and scarcer, but seizure reduction and safety profiles are similar to adults. Further studies are needed to optimize neuromodulation target selection and programming paradigms.

Background: For women in the United States desiring implant-based breast reconstruction, the maximum volume of commercially available silicone breast implants has, to date, been 800 cc. The authors evaluated the safety of Mentor larger-size MemoryGel Ultra-High Profile breast implants (≥930 cc) in women undergoing postmastectomy breast reconstruction. Methods: This 10-year, multicenter, open-label, prospective, investigational study assessed the safety and effectiveness of larger-volume implants in women undergoing postmastectomy 2-stage primary or revision reconstruction. Three-year Kaplan-Meier curves and multivariable Cox regression analyzed the association between patient and treatment characteristics and surgical outcomes. The BREAST-Q was used to assess effectiveness at 3 years. Results: A total of 400 women were enrolled (225 primary and 175 revision reconstruction). Mean body mass index was 35.8 kg/m 2 ; 81% had a body mass index greater than or equal to 30 kg/m 2 . At 3 years, the cumulative incidence of postoperative complications (excluding rupture) was 63.0% (95% confidence interval, 58.1% to 67.9%). The reoperation rate was 24.5% (95% CI, 20.4% to 29.1%), and the explantation rate was 15.8% (95% CI, 23.5% to 19.9%). Independent predictors of complications included radiation therapy and a history of smoking. There were no significant associations between postoperative complications and implant volume greater than or equal to 1135 cc or less than 1135 cc. BREAST-Q scores at year 3 showed significant improvements from baseline in Satisfaction with Breasts, Psychosocial Well-Being, Sexual Well-Being, and Physical Well-Being (all P < 0.0001). The mean score for Satisfaction with Outcome was 79.9. Conclusions: These 3-year results demonstrate that larger-volume silicone breast implants represent a safe and effective option for patients with larger breasts who are requesting postmastectomy implant-based reconstruction.

OBJECTIVE Core outcome sets (COSs) are needed to promote data consistency across studies as well as data synthesis and comparability. The aim of the current study was to use a modified Delphi process to develop a COS for lateral femoral cutaneous neuropathy (LFCN), hereafter COS-LFCN. METHODS A 5-stage approach was used to develop the COS-LFCN: 1) consortium development, 2) literature review to identify potential outcome measures, 3) Delphi survey to develop consensus on outcomes for inclusion, 4) Delphi survey to develop definitions, and 5) consensus meeting to finalize the COS and definitions. This study followed the Core Outcome Set - STAndards for Development, (COS-STAD) recommendations. RESULTS The Core Outcomes in Nerve Surgery (COINS) Consortium comprised 25 participants, all neurological surgeons, representing 14 countries. The final COS-LFCN consisted of 41 factors and outcomes covering domains of demographics, diagnostics, patient-reported outcomes, motor/sensory outcomes, and complications. Appropriate instruments, methods of testing, and definitions were set. The consensus minimum duration of follow-up was 12 months, with the consensus optimal time points for assessment being preoperatively and 3, 6, and 12 months postoperatively. CONCLUSIONS The COINS Consortium developed a consensus COS for LFCN and provided definitions, methods of implementation, and time points for assessment. The COS-LFCN should serve as the minimum data to be collected in all future neurosurgical studies on LFCN. Incorporation of this COS will help improve consistency in reporting, data synthesis and comparability, and minimize outcome reporting bias.

Early allograft failure (EAF) after living donor liver transplantation (LDLT) remains a clinical challenge. Existing prediction models developed for deceased donor transplantation poorly apply to LDLT due to distinct surgical and physiological factors. This study identifies clinical determinants of EAF and develops an LDLT-specific prediction model. We conducted a multicenter retrospective cohort study from 17 high-volume LDLT centers (January 2016–December 2020) with external validation at a tertiary center in Saudi Arabia (January 2015–December 2022). The primary outcome was EAF (graft loss or patient death ≤90 d). Multivariable mixed-effects logistic regression identified preoperative/intraoperative risk factors. The EAGLE-LDLT model was constructed using postoperative laboratory values. Performance was compared against established models (EAD, MEAF, A2ALL) using ROC analysis and decision curve analysis. The development cohort included 2944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with a 5.5% EAF rate. External validation included 1020 recipients (median MELD 21, 6.7% EAF). Independent risk factors for EAF were MELD (OR 1.06, 95% CI 1.04–1.08), donor BMI (OR 1.05, 95% CI 1.00–1.10), portal vein thrombosis (OR 1.73, 95% CI 1.13–2.63), and hepaticojejunostomy (OR 1.58, 95% CI 1.06–2.36). The EAGLE-LDLT model incorporating peak ALT (>468 U/L), peak INR (>1.9), and bilirubin (>3.5 mg/dL) and INR (>1.3) at POD7, demonstrated superior discrimination (AUC=0.81) compared with MEAF (AUC=0.77, p =0.004), EAD (AUC=0.67, p <0.001), and A2ALL (AUC=0.65, p <0.001). EAGLE-LDLT achieved balanced sensitivity (75.0%) and specificity (73.7%), effectively stratifying patients into high-risk (15% of patients; 40.4% EAF incidence) and low-risk groups. Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at the highest risk for EAF postoperatively.

Abstract Background Global data demonstrate an inverse relationship between hospital ileoanal pouch volume and adverse outcomes, highlighting the need for quality metrics for this technically demanding procedure.1-4 Currently, there are no standardised quality indicators to define high-quality pouch surgery. This study aimed to establish international consensus on key performance indicators (KPIs) in pouch surgery. Methods A modified Delphi process was conducted among an international, multidisciplinary panel of experts. Participants were identified through professional networks, literature review, and direct email invitation; additional participants recruited via a snowball sampling approach over one month. Candidate KPIs were generated from literature review and expert input across 5 domains. Each item was rated on a 9-point Likert scale (1 = not important, 9 = critically important); consensus was predefined as ≥ 80% of respondents rating an item 7–9. Items not reaching consensus were re-rated in a second round following anonymized group feedback. Results Round 1 included 312 participants from 37 countries. Demographics (Table 1): 53% were age 45 years, 35.2% were female, and included surgeons (68.6%) and gastroenterologists (31.1%). Most (76.6%) practice at academic or university hospitals, with a median of 11 (5–17) years in practise; 78.9% perform &gt;1 pouchoscopy per month, and 39.5% of surgeons perform 5 pouches/year. Respondents’ regions included North America (51%), Europe (26%), Oceania (9%), Asia (6%), Latin America (4%), and the Middle East/Africa (4%). Seven (18%) countries (Denmark, Lithuania, Malaysia, Norway, United Arab Emirates, Sweden, and Switzerland) reported pouch surgery is already centralised, while 70.6% agreed pouch centralisation should be implemented. Cost/logistics and professional autonomy were the most important barriers to centralisation, whereas capacity and training exposure were least important. Overall, 37/59 (62.7%) candidate KPIs reached consensus after Round 1. The Round 2 response rate was 61%, and 2 additional KPIs reached consensus (preop ileitis and long-term risk of pouchitis). Additionally, 14 of 25 (56%) informed consent items reached consensus, and 64% of respondents felt informed consent for pouch surgery requires 2 or more office visits. Consensus items were used to populate a KPIPS Checklist (Figure 1). Conclusion Through an international Delphi process, the KPIPS study identified a consensus-derived set of key performance indicators to measure quality in pouch surgery. These quality metrics provide a foundation for benchmarking outcomes and represent a critical step towards centralization and the development of designated pouch centres of excellence to improve global standards of care for pouch patients. References: 1. Worley GHT, Fearnhead NS, Brown SR, et al. Review of current practice and outcomes following ileoanal pouch surgery: Lessons learned from the ileoanal pouch registry and the 2017 ileoanal pouch report. Colorectal Dis 2018;20:913–22. 2. Giddings HL, Yang PF, Steffens D, Solomon MJ, Ng KS. Influence of hospital-level and surgeon factors on the outcomes after ileo-anal pouch surgery for inflammatory bowel disease: Systematic review. Br J Surg 2024;111. 3. Hoang CM, Maykel JA, Davids JS, et al. Distribution of elective ileal pouch-anal anastomosis cases for ulcerative colitis: A study utilizing the university health system consortium database. J Gastrointest Surg 2020;24:2613–9. 4. Moojen TB, Visser E, Reijntjes MA, et al. One-year stoma-free survival of ileoanal pouches for uc in european centers: The miracle project. Ann Surg Open 2025;6:e596. Conflict of interest: Prof. Holubar, Stefan: No conflict of interest Soop, Mattias: No conflict of interest Poskus, Tomas: No conflict of interest Neary, Peter: No conflict of interest Uchino, Tairin: No conflict of interest Yang, Song-soo: No conflict of interest Adamina, Michel: No conflict of interest Avellaneda, Nicolas Luis: No conflict of interest Barnes, Edward: No conflict of interest Brouquet, Antoine: No conflict of interest Brown, Steven Ross: No conflict of interest De Buck Van Overstraeten, Anthony: No conflict of interest Buskens, Christianne J.: No conflict of interest Chiorean, Michael: No conflict of interest Clark, David: No conflict of interest Cohen, Benjamin: No conflict of interest El Ouali, Sara: No conflict of interest Faiz, Omar Darius: No conflict of interest Fichera, Alessandro: No conflict of interest Hahnloser, Dieter: No conflict of interest Hancock, Laura: No conflict of interest Herfarth, Hans: No conflict of interest Johnston, Michael: No conflict of interest Kayal, Maia: No conflict of interest Laharie, David: No conflict of interest Liberman, Sender: No conflict of interest Loftus, Jr, Edward: No conflict of interest Moeslein, Gabriela: No conflict of interest Myrelid, Pär: No conflict of interest Poylin, Vitaliy: No conflict of interest Qazi, Taha: No conflict of interest Raffals, Laura: No conflict of interest Rubin, David T.: No conflict of interest Spinelli, Antonino: No conflict of interest Steinhagen, Emily: No conflict of interest Tozer, Philip: No conflict of interest Verstockt, Bram: No conflict of interest Warusavitarne, Janindra: No conflict of interest Wexner, Steven: No conflict of interest Winter, Desmond: No conflict of interest

Abstract Background We aimed to describe the real-world durability of clinical and endoscopic outcomes of upadacitinib (UPA) in ulcerative colitis (UC). Methods We retrospectively analyzed an ongoing multi-center cohort of UC patients(pts) in the United States who were started on UPA for active disease. Primary outcome was clinical remission at 1yr assessed by partial Mayo score(pMS)(resolution of all UC related symptoms and physician global assessment). Secondary outcomes assessed at various timepoints, including after re-escalation, included clinical response(&gt;50%reduction in symptoms utilizing the pMS), endoscopic response (Mayo endoscopic score≤1or absence of erosions/ulcerations) and histological remission(defined as normal or chronic inactive). Durable secondary outcomes achieved at both 6months and at 1yr and beyond were also assessed. Descriptive statistics were performed. Adverse events(AEs) investigated included cardiovascular events and herpes zoster (HZ). Results 416pts were included(Table 1) with median values for age at initiation of36.00yrs(range27-48), disease duration7.00yrs(range3-14)and follow-up 993days(IQR,672-1103), with206(49.52%)with follow-up at 1yr or beyond. Of these, 66.1%had extensive colitis. Induction duration was8wks in 86.5%and16wks in 4.8 %(20/416). Most received an induction dose of45mg(97.1%)and maintenance dose of30mg(85.8%). Among the evaluable population at1yr and beyond, 62.5%(45/80)were in clinical remission, 75.9%(22/29)in endoscopic response with58.6%(17/29) in remission and66.6%(20/30)in histological remission. At 6months, clinical remission was51.6%(42/93), endoscopic response was 75%(24/32) of which43.75%(14/32)were in remission and histologic remission was seen in 40.8%(20/49).Durable secondary outcomes of clinical remission were seen in 71.4%(30/42), endoscopic improvement in 45.8%(11/24), and histologic remission in35%(7/20). Re-escalation to 45mg occurred in 35pts, of which48.6%(17/35)were re-escalated due to incomplete induction clinical response with all achieving response(11 in remission). The rest(18/35)were re-escalated for clinical relapse after initial remission where72.2%(13/18)were able to recapture remission. After re-escalation,54.3%(19/35)continued on 45mg daily. AEs were reported in12.7%with HZ in 7pts(2.1%). One pt aged53yrs had an episode of TIA after 1yr on UPA at 15mg, without pre-existing cardiovascular risk factors and UPA use is ongoing at the same dose. There were no reported episodes of worsening of pre-existing cardiovascular disease or DVT/PE. Conclusion UPA is durably effective and safe in a real-world clinical setting with re-escalation to 45mg capturing response in a majority of the pts. Ongoing recruitment will provide further evidence on long term effectiveness and safety. Conflict of interest: Ms. Huang, Katherine: No conflict of interest Chandnani, Arun: No conflict of interest Samaan, Sami: None Tan, Tingyi: I have no conflict of interests to declare. Ramesh, Prajith Raj: None Conlon, Caroline: No conflict of interest Klein, Jeremy: None Reddy, Nikhil: No conflict of interest Ayoub, Malek: None Cervantes Limon, Gabriela: none Myers, Melissa: None Bucaram, Martha: No conflict of interest Lieto, Stephen: No conflict of interest Scalzo, Nicholas: No conflict of interest Cohen, Benjamin: Abbvie - consulting and speaking ALPCO - consulting J & J Innovative Medicine - consulting Takeda - consulting and speaking Emmes Biopharma Services LLC - DSMB Pfizer - consulting Mohamed, Islam: nothing to declare Ciorba, Matthew: Advisory Board: Janssen, AbbVie, Pfizer, Geneoscopy Consulting: Janssen, AbbVie, Pfizer, Geneoscopy Grant contracts: Janssen, Pfizer, Incyte Shukla, Richa: Speakers bureau - Abbvie and Lilly Wright, Michael: None Xu, An Tao: No conflict of interest Chowla, Navreet: No conflict of interest Ogholikhan, Sina: Abbvie Medical Director Kujawski, Michelle: Abbvie Scientific Director Dulaney, David: Speaker bureau - Regeneron Speaker bureau - Sanofi Speaker bureau - Takeda Speaker bureau - Eli Lilly Fenster, Marc: none Benson, Caroline: No conflict of interest Alhobayb, Tamara: No conflict of interest Berinstein, Jeffrey: No conflict of interest Shivashankar, Raina: Abbvie and BMS - speaker bureau Janssen and Celltrion - grant for IBD fellowship funding Pfizer - consultant Bishu, Shrinivas: None Patel, Anish: speaker: abbvie, J & J, Lilly, BMS, Pfizer, Phathom, Takeda consultant: abbvie Ungaro, Ryan: Personal Fees: AbbVie, Bristol Myers Squibb, Genentech, Lilly, Pfizer, Janssen, Takeda Johnson, Amanda: Research grant: Abbvie, Spyre Therapeutics Pekow, Joel R.: CVS Health - Consulting Abbvie, Eli Lilly, Pfizer, Johnson and Johnson - Stocks Yarur, Andres: Personal Fees: Consultant for Takeda, Pfizer, Roche, Merck, Abbvie, Eli Lilly. Bristol Myers Squibb, Celltrion, Johnson and Johnson. Deepak, Parakkal: Parakkal Deepak has received research support under a sponsored research agreement unrelated to the data in the abstract from AbbVie, Johnson and Johnson, Sanofi, Merck, Teva, Direct Biologics, Tr1x, Boehringer Ingelheim, Bristol Myers Squibb, Pfizer, Prometheus Biosciences, Takeda Pharmaceuticals, Roche Genentech, Eli Lilly, AstraZeneca, Spyre and Agomab, has received consulting fees from Johnson and Johnson, Abbvie, Merck, Sobi, Celltrion, Fresenius Kabi, Asahi Kasei Pharma, Sandoz and CorEvitas, LLC and has served on the board of the Srategic Alliance for Intercultural Advocacy in GI.

Abstract Background Colonic strictures in Crohn’s disease (CD) present a complex and relatively underexplored clinical issue. Imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and intestinal ultrasound (IUS), have established criteria for evaluating small bowel CD strictures. However, radiologic definitions of benign colonic strictures and their diagnostic accuracy are not well-developed. Endoscopic definitions of colonic strictures are the most well-described. We conducted a systematic review to summarize definitions, diagnosis, and identification of inflammation and fibrosis of colonic strictures on endoscopy, CT, MR, and IUS. Methods A systematic review following PRISMA guidelines, from inception to October 6, 2024, was conducted using MEDLINE, Scopus, CINAHL, and CENTRAL databases to identify literature on diagnostic imaging of patients with colonic CD strictures. Only studies with comparison to endoscopy or imaging to a full-thickness histologic gold standard to differentiate fibrosis from inflammation were included. Two stage screening was completed in duplicate, and risk of bias was assessed using QUADAS-2. Results 46 studies were eligible for inclusion of 4623 abstracts screened. Definitions of colonic strictures on CT, MR, and IUS were highly variable with combinations of bowel wall thickness, luminal narrowing, and pre-stenotic dilation. Endoscopic definitions of colonic strictures were the most consistently reported as lack of passage of a colonoscope through a narrowed lumen. Diagnostic imaging definitions used various combinations of increased bowel wall thickness, luminal narrowing, and pre-stenotic dilation with no clear cutoffs. Few studies directly compared imaging features with histologic or endoscopic gold standards, limiting evaluation of diagnostic accuracy. Meta-analyses were not conducted due to methodological heterogeneity of grading fibrosis across studies. In addition, statistical heterogeneity of sensitivity, specificity, and accuracy of colonic stricture diagnoses was limited by pooling with small bowel strictures in the studies. Conclusion This systematic review highlighted the absence of standardized definitions for diagnosing colonic Crohn’s disease strictures, particularly on diagnostic imaging, and the limited data distinguishing inflammatory from fibrotic components. Bowel wall thickening, luminal narrowing, and pre-stenotic dilation are key features of small bowel strictures, but their relevance to the colon requires further assessment. Establishing standardized definitions for colonic strictures that incorporate both endoscopic and imaging modalities is essential to advancing the field. Conflict of interest: Dr. Lu, Cathy: Advisory board - Abbvie, Johnson and Johnson, Takeda, Ferring, Merck, Celltrion, Lilly, Pfizer, Pendopharm, Agomab. Research Funding - Abbvie, Helmsley Charitable Trust, Alberta Innovates Dhaliwal, Ravdip: No conflict of interest Kellar, Amelia: No conflict of interest Rowan, Catherine: No conflict of interest St-Pierre, Joelle: Consultant for Pfizer, Abbvie, Eli Lilly and Pendopharm, and speaker for Takeda. Ernest-Suárez, Kenneth: Consulting/Advisory Board fees: Abbvie, AstraZeneca, Johnson & Johnson, Pfizer, Ferring, Sandoz, SatisfAI, Takeda O’Brien, Maureen: No conflict of interest Rosentreter, Ryan: No conflict of interest Gulhati, Vishrut: No conflict of interest Baker, Mark: No conflict of interest Bettenworth, Dominik: DB is on the advisory board or consultant for AbbVie, Amgen, Arena, Atheneum, BNG Service GmbH, Bristol Myers Squibb, CED-Service GmbH, Celltrion, Doctorflix, DGVS, Diaplan, Else Kröner-Fresenius Foundation, Falk Foundation, Galapagos, Guidepoint, Impulze, Ferring, Janssen Cilag, Lilly, Medical Tribune, MedTriX, MSD, Mylan, Onkowissen, Pharmacosmos, Pfizer, Roche, Sandoz, Takeda, Tetrameros, Thieme, Tillotts Pharma, UCB Biopiharma, Viatris and Vifor Pharma. Bruining, David: Advisory board fees from Janssen Bari, Dane: Grant from Helmsley Charitable Trust speaker honorarium from Siemens Healthineers support for attending meetings or travel from Siemens Healthineers member of the Olympus advisory board co-chair Society of Abdominal Radiology (SAR) Inflammatory Bowel Disease Disease Focused Panel (IBD DFP) and SAR Photon-Counting Detector CT Emerging Technology Commission and chair of the Society for Advanced Body Imaging Scientific Committee. Dillman, Jonathan: Grants from Philips Healthcare, GE HealthCare, Motilent, Perspectum, Guerbet, and Bracco. El Ouali, Sara: Speaker and/or consulting fees from Takeda, Eli Lilly, Janssen, Abbvie. Fletcher, John: Grant supports from Siemens Healthineers Gordon, Ilyssa: Grants to institution from Morphic Therapeutics, Alimentiv, Pliant Therapeutics, and UCB patent pending with Case Western Reserve University on radiomics signatures Jairath, Vipul: Consulting Fees: Abbvie, Alimentiv, Amgen, Anaptys Bio, Asahi Kasei, Asieris, Astra Zeneca, Attovia, Blackbird Labs, BMS, Boehringer Ingleheim, Biomebank, Caldera, Calluna, Catalytic Health, Celltrion, Ensho, Enthera, Exeliome Biosciences, Ferring, Fresenius Kabi, Gilead, Granite Bio, GSK, Janssen, Lilly, Merck, Mountainfield, MRM Health, Nxera, Organon, OSE Immunotherapeutics, Pendopharm, Pioneering Medicine, Pfizer, Prometheus, Roche/Genentech, Sanofi, SCOPE, Shattuck Labs, Sorriso, Spyre, Synedgen, Takeda, Teva, Tillotts, Union Therapeutics, Ventus, Ventyx, Vividion, Xencor, Zealand Pharma. Feagan, Brian Gordon: Consulting Fees: AbbVie, Abivax, Adaxion, Adiso, AgomAB Therapeutics, Akros, Alira Health, Ally Bridge Group, Apini Therapeutics, Argenx, Attovia Tx, Avoro Capital Advisors, Belmore Law, Biora Therapeutics, Blackbird Laboratories, Boehringer-Ingelheim, BMS, Boxer Capital, Celgene/BMS, Clarivate, Connect Biopharm, EcoR1, Eli Lilly, Ensho Therapeutics, Equillium, Evida, Enveda, Evommune Inc. Faes Farma, First Wave, Forbion, Galapagos, Genentech/Roche, General Atlantic, Genesis Therapeutics, Gerson Lehrman Group, Gilead, Guidepoint, Imhotex, ImiDomics, Immunic Therapeutics, Janssen, Japan Tobacco Inc., LifeMine Therapeutics, Mage Biologics, Merck, Mirador Therapeutics, Mobius Care, Monte Rosa Tx, Morphic Therapeutics, Nimbus Therapeutics, Novartis, Nxera, OncoC3, Palisade Bio, Pendopharm, Pfizer,Q32 Bio, REDX, Roche, Sanofi, Sobi, Sorriso, Spyre Therapeutics, Sun Pharma, Surrozen Inc., Synedgen, Takeda, Tegus Inc., Teva, Tillotts, Trex Bio, TR1X Inc. TVM Lifesciences, Ventyx Biosciences, Versant Ventures, Vida Ventures, Ysios Capital, Zagbio Stock Shareholder: Connect Biopharm, EnGene, Evida, SRT, Imidomics, Enveda Rieder, Florian: Personal Fees: Adiso, Adnovate, Agomab, Allergan, AbbVie, Arena, Astra Zeneca, Boehringer-Ingelheim, Celgene/BMS, Celltrion, CDISC, Celsius, Cowen, Ferring, Galapagos, Galmed, Genentech, Gilead, Gossamer, Granite, Guidepoint, Helmsley, Horizon Therapeutics, Image Analysis Limited, Index Pharma, Landos, Jannsen, Koutif, Mestag, Metacrine, Mopac, Morphic, Organovo, Origo, Palisade, Pfizer, Pliant, Prometheus Biosciences, Receptos, RedX, Roche, Samsung, Sanofi, Surmodics, Surrozen, Takeda, Techlab, Teva, Theravance, Thetis, UCB, Ysios, 89Bio

Abstract Background Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by immune dysregulation1,2. Blood-based biomarkers in diagnosing patients with IBD are lacking. We tested the hypothesis that circulating immune cell types and immune repertoire could distinguish patients with IBD from controls. Methods We performed deep immunophenotyping with scRNA-seq with T-cell receptor (TCR) and B-cell receptor (BCR) sequencing on peripheral blood mononuclear cells (PBMCs) from patients with IBD and healthy controls (87 UC, 127 CD, 35 controls). We applied machine learning to quantify cell abundance (miloR3), construct diagnostic classifiers (immuneML4), and infer cell-cell ligand-receptor interactions (MultiNicheNet5) between patients with IBD and controls. Results A scRNA-seq atlas of over 1.1 million PBMCs consisting of 53 distinct cell types was generated (Fig1A). In classifying UC vs controls, support vector machine model had a balanced accuracy of 0.90, consisting of increased IgA and IgG plasma B cells, switched memory B cells, TRegs, and decreased CD16 monocytes, cDC1, non-switched B cells, and MAIT (Fig.1B). In classifying CD vs controls, K-nearest neighbors model had a balanced accuracy of 0.87, consisting of increased IgA plasma B cells, non-switched B cells, CD4 Th22, TReg cytotoxic, CD4 Th1/Th17, and pDC and decreased CD16 monocytes, gdT, CD4 Th17, CD4 Th2, and MAIT cells.Fig.1C). Inflammation was associated with increased IgG plasma B in patients with both UC and CD. Using 4-mer amino acid motif of the BCR complementary-determining region 3 (CDR3) logistic regression model, immuneML had a balanced accuracy of 0.82 (0.75-0.92) in classifying CD vs controls (Fig.1D with models showing top 25 BCR predictive features). Using 4-mer amino acid motif of the BCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.90 (0.70-1.00) in classifying UC vs controls (Fig.1E with models showing top 25 BCR predictive features). Using 3-mer amino acid motif of the TCR CDR3 logistic regression model, immuneML had a balanced accuracy of 0.76 (0.72-081) in classifying CD vs UC. (Fig.1F with models showing top 25 TCR predictive features). In UC vs controls, MultiNicheNet demonstrated increased signaling in ADAM10-NOTCH2 between TRegs and naïve B cells and IL15-IL2RA between CD14 monocytes and CD4 Th17 cells. In CD vs controls, there was increased signaling in IL15-IL2RA between CD14 monocytes and CD4 Th17 cells and ADAM10-TSPAN5 between CD14 monocytes and TRegs (Fig.1G). Conclusion Applying machine learning models to scRNA-seq and immune repertoire datasets enable blood-based deep immunophenotyping with diagnostic potential and reveals novel insights into cell signaling in IBD. References: 1.Kong L, Pokatayev V, Lefkovith A, Carter GT, Creasey EA, Krishna C, Subramanian S, Kochar B, Ashenberg O, Lau H, Ananthakrishnan AN, Graham DB, Deguine J, Xavier RJ. The landscape of immune dysregulation in Crohn’s disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity. 2023 Feb 14;56(2):444-458.e5. doi: 10.1016/j.immuni.2023.01.002. Epub 2023 Jan 30. Erratum in: Immunity. 2023 Dec 12;56(12):2855. doi: 10.1016/j.immuni.2023.10.017. PMID: 36720220; PMCID: PMC9957882. 2.Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G, Graham DB, Herbst RH, Rogel N, Slyper M, Waldman J, Sud M, Andrews E, Velonias G, Haber AL, Jagadeesh K, Vickovic S, Yao J, Stevens C, Dionne D, Nguyen LT, Villani AC, Hofree M, Creasey EA, Huang H, Rozenblatt-Rosen O, Garber JJ, Khalili H, Desch AN, Daly MJ, Ananthakrishnan AN, Shalek AK, Xavier RJ, Regev A. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019 Jul 25;178(3):714-730.e22. doi: 10.1016/j.cell.2019.06.029. PMID: 31348891; PMCID: PMC6662628. 3.Dann E, Henderson NC, Teichmann SA, Morgan MD, Marioni JC. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol. 2022 Feb;40(2):245-253. doi: 10.1038/s41587-021-01033-z. Epub 2021 Sep 30. PMID: 34594043; PMCID: PMC7617075. 4.Pavlović M, Scheffer L, Motwani K, Kanduri C, Kompova R, Vazov N, Waagan K, Bernal FLM, Costa AA, Corrie B, Akbar R, Al Hajj GS, Balaban G, Brusko TM, Chernigovskaya M, Christley S, Cowell LG, Frank R, Grytten I, Gundersen S, Haff IH, Hovig E, Hsieh PH, Klambauer G, Kuijjer ML, Lund-Andersen C, Martini A, Minotto T, Pensar J, Rand K, Riccardi E, Robert PA, Rocha A, Slabodkin A, Snapkov I, Sollid LM, Titov D, Weber CR, Widrich M, Yaari G, Greiff V, Sandve GK. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell. 2021 Nov;3(11):936-944. doi: 10.1038/s42256-021-00413-z. Epub 2021 Nov 16. PMID: 37396030; PMCID: PMC10312379. 5.Browaeys, R., Gilis, J., Sang-Aram, C., De Bleser, P., Hoste, L., Tavernier, S., Lambrechts, D., Seurinck, R. and Saeys, Y., 2023. MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. BioRxiv, pp.2023-06. Conflict of interest: Dr. Gubatan, John Mark: No conflict of interest Jiayu, Ye: No conflict of interest Christin, Lund-Andersen: No conflict of interest Boye, Theresa: No conflict of interest Hoang, Jacqueline: No conflict of interest Sojwal, Raoul: No conflict of interest Fardeen, Touran: No conflict of interest Tran, Tracy: No conflict of interest Sokzini, Peacha: No conflict of interest Koh, Ally: No conflict of interest Gibson, Marielle: No conflict of interest Sinha, Sidhartha: No conflict of interest Rogalla, Stephan: No conflict of interest Rosen, Michael: No conflict of interest Nielsen, Ole Haagen: No conflict of interest Sandve, Geir Kjetil: No conflict of interest

Abstract Background Icotrokinra is the first targeted oral peptide that selectively blocks the IL-23 receptor (IL-23R) and precisely inhibits the IL-23 pathway. In ANTHEM-UC—a Phase 2b, randomised, double-blind, placebo (PBO)-controlled, dose-ranging study in adults with moderately to severely active ulcerative colitis (UC)—all doses of icotrokinra achieved the primary endpoint of clinical response and delivered clinically meaningful improvements across key secondary endpoints at Week (W)12,1 which were maintained or improved at W28.2 Here we report the impact of icotrokinra on systemic and tissue biomarkers of inflammatory burden in UC. Methods Participants were randomised (1:1:1:1) to receive once-daily icotrokinra 100 mg, 200 mg, 400 mg, or PBO. Clinical inflammatory biomarkers, C-reactive protein (CRP) and faecal calprotectin (FCP), along with serum IL-23 pathway biomarkers (IL-22, IL-17A, and IL-17F) were used to determine the impact of icotrokinra on systemic inflammation. Gene expression changes in colonic biopsies were used to confirm tissue IL-23 pathway engagement and dampening of tissue inflammation with icotrokinra. Results At the W12 primary timepoint, all doses of icotrokinra attenuated levels of serum CRP and FCP (Figure 1). All doses of icotrokinra, but not PBO, markedly reduced serum levels of IL-22, IL-17A, and IL-17F at W12 relative to baseline. Greater reductions in these serum biomarkers of IL-23 pathway activation were observed with the 200 mg and 400 mg doses relative to the 100 mg dose (Figure 1, nominal p &lt; 0.05). All doses of icotrokinra, but not PBO, markedly reduced expression of gene sets associated with the IL-23–driven inflammation in tissue at W12 relative to baseline (Figure 2, nominal p &lt; 0.0001). With icotrokinra treatment, the tissue transcriptome more closely resembled that of healthy individuals, with decreased expression of gene sets associated with UC-relevant inflammatory cell types and increased expression of genes characteristic of healthy epithelial cells. Importantly, dampening of systemic and tissue inflammation with icotrokinra was sustained at W28 (Figures 1 & 2). Conclusion We show for the first time that blocking the IL-23R can inhibit IL-23-mediated inflammatory signaling in UC patients. Icotrokinra treatment induced a sustained (W28) reduction in systemic and tissue biomarkers of inflammatory burden in UC. Furthermore, icotrokinra induced transcriptional changes that are consistent with dampening of IL-23–driven inflammation and normalization of the tissue transcriptome. While all once-daily doses of icotrokinra attenuated systemic and tissue inflammation, greater reduction of systemic inflammatory burden was observed with the 200 mg and 400 mg doses of icotrokinra. References: 1. Abreu MT, et al. United European Gastroenterol J. 2025 10;13(suppl 8):172-3. 2. Jairath V, et al. Am J Gastroenterol. 2025 120;10(suppl 2):S312. Conflict of interest: Louis, Edouard: Education and Research Grants for my department: Abbvie, Takeda, Janssen, Pfizer, Fresenius-Kabi, Celltrion, EG pharma, Sandoz, Falk Personal Fees for conferences, advisory boards and consultancy: Abbvie, Takeda, Ferring, Pfizer, Janssen, Lilly, Galapagos, Celltrion, Arena, BMS, Falk, Biokuris, Fresenius-Kabi, Thabor Jairath, Vipul: Consulting fees from AbbVie, Alimentiv, Amgen, Anaptys Bio, Asahi Kasei, Asieris, Astra Zeneca, Attovia, Blackbird Labs, Bristol Myers Squibb, Boehringer Ingleheim, Biomebank, Caldera, Calluna, Catalytic Health, Celltrion, Ensho, Enthera, Exeliome Biosciences, Ferring, Fresenius Kabi, Gilead, Granite Bio, GSK, Johnson & Johnson, Lilly, Merck, Mountainfield, MRM Health, Nxera, Organon, OSE Immunotherapeutics, Pendopharm, Pioneering Medicine, Pfizer, Prometheus, Roche/Genentech, Sanofi, SCOPE, Shattuck Labs, Sorriso, Spyre, Synedgen, Takeda, Teva, Tillotts, Union Therapeutics, Ventus, Ventyx, Vividion, Xencor, Zealand Pharma. Siegmund, Britta: Consultant for AbbVie, Abivax, Boehringer Ingelheim, Bristol Myers Squibb, Dr. Falk Pharma, Eli Lilly, Endpoint Health, Galapagos, Johnson & Johnson, Materia Prima, MSD, Pfizer, Takeda, Wedbush Securities speaker for AbbVie, AlfaSigma, Bristol Myers Squibb, CED Service GmbH, Dr. Falk Pharma, Eli Lilly, Ferring, Galapagos, Johnson & Johnson, MD Education, MSD, Pfizer, Tr1xBio grant from Pfizer. Kannan, Arun K: Employee and shareholder of Johnson & Johnson Zeeman, Martha: Employee and shareholder of Johnson & Johnson Hart, Amy: Employee and shareholder of Johnson & Johnson Strawn, David: Employee and shareholder of Johnson & Johnson Venkat, Swati: Employee and shareholder of Johnson & Johnson Tomsho, Lynn: Employee and shareholder of Johnson & Johnson Mcrae, Bradford: Employee and shareholder of Johnson & Johnson Ruane, Darren: Employee and shareholder of Johnson & Johnson Surace, Lindsey: Employee and shareholder of Johnson & Johnson Erondu, Ngozi: Employee and shareholder of Johnson & Johnson Zhan, Joyce: Employee and shareholder of Johnson & Johnson Chen, Minhu: I have received speaker honoraria from Takeda China, Xian Janssen, and AbbVie China, as well as research funding from Takeda (China). Chachu, Karen: Advisory board for Johnson & Johnson consultant for OptumRx Matsuoka, Katsuyoshi: Grant: Abbvie Inc., Mochida Pharmaceutical Co., Ltd., ZERIA Pharmaceutical Co., Ltd., Nippon Kayaku Co., Ltd., JIMRO Co., Ltd. Personal Fees: Mitsubishi Tanabe Pharma, Janssen Pharmaceutical K.K., Abbvie Inc., EA Pharma Co., Ltd., Takeda Pharmaceutical Co., Ltd., Mochida Pharmaceutical Co., Ltd., ZERIA Pharmaceutical Co., Ltd., Nippon Kayaku Co., Ltd., Pfizer Inc., Kyorin Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Kissei Pharmaceutical Co., Ltd., Gilead Sciences, Celltrion Healthcare, Bristol-Myers Squibb Limdi, Jimmy: Speaker/consultancy fees from AbbVie, Abivax, Arena, BioHit, Bristol Myers Squibb, Celltrion, Eli Lilly, Ferring, Galapagos, Johnson & Johnson, MSD, Pfizer, Takeda research support from Galapagos, Takeda Rydzewska, Grazyna: Speaker and consultant fees from AbbVie, AlfaSigma, AstraZeneca, Bayer, Biocodex, Bristol Myers Squibb, Ferring, Johnson & Johnson, Lilly, PRO.MED.Pl, Recordati, Sanprobi, SOBI, Takeda travel/accommodation meeting expenses from AbbVie, Ferring, Takeda Abreu, Maria Teresa: Grant/Research Support: Janssen, Prometheus Bioscience, Takeda Consultant: Janssen, Prometheus Bioscience, Takeda Gilead, Eli Lilly, AbbVie Inc., Bristol Myers, Imedex, Loftus Jr, Edward V: Consultant/advisory board member for AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly and Company, Genentech, Gilead Sciences, Johnson & Johnson, Pfizer, Takeda, UCB.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.