Introduction

Cendakimab is a recombinant, humanized, high-affinity, neutralizing monoclonal antibody that binds to interleukin 13 (IL-13) and blocks interaction with both its receptors, IL-13Rα1 and IL-13Rα2. IL-13 is a cytokine implicated in the pathogenesis of atopic dermatitis (AD) and is thought to be a key driver of the complex pathophysiology of eosinophilic esophagitis (EoE). IL-13 signals through both IL-13Rα1 and IL-13Rα2 to regulate cellular and molecular pathways across a variety of cell types. The binding of IL-13 to IL-13Rα1 promotes a Type 2 immune response and eosinophil recruitment, whereas the binding of IL-13 to IL-13Rα2 is believed to play a role in fibrosis and epithelial tissue remodeling. Several biomarkers associated with Type 2 inflammatory diseases are known to be regulated by IL-13. Eotaxin-3, a key biomarker, plays a central role in attracting eosinophils to the site of inflammation. Periostin, an extracellular matrix protein linked to activated fibroblasts, is implicated in tissue remodeling and fibrosis. Another enzyme governed by IL-13 is matrix metalloproteinase-12 (MMP-12)—this enzyme contributes to tissue remodeling and breakdown of extracellular matrix components. Here, we evaluate the pharmacodynamic effects of IL-13 inhibition with cendakimab on the key serum biomarkers eotaxin-3, periostin, and MMP-12 in patients with AD, with a focus on exploring potential implications for EoE.

Aims & Methods

In this phase 2, multicenter, global, randomized, double-blinded, placebo-controlled, parallel-group study (NCT04800315), 220 adults with moderate-to-severe AD received cendakimab 360 mg Q2W, 720 mg QW, 720 mg Q2W, or placebo for 16 weeks. Blood samples were collected, and a panel of biomarkers were assessed. The current analysis focuses on the assessment of serum levels of eotaxin-3, periostin, and MMP-12 using Proximity Extension Assay pre- and post-treatment on day 1 (baseline), at week 1 and week 2, and then biweekly until week 16. The least square mean percent change from baseline at week 16 for each treatment group was evaluated.

Results

For all 3 doses of cendakimab, there was a significant decrease from baseline in the levels of eotaxin-3, periostin, and MMP-12 as early as week 1 post-treatment and continued through week 16 of cendakimab treatment (Table). Compared with placebo, cendakimab resulted in significantly greater reductions in these biomarker levels through week 16.

Conclusion

Cendakimab decreased levels of key inflammatory and remodeling biomarkers over 16 weeks of treatment in patients with AD. These findings suggest that cendakimab modulates pathways of inflammation, fibrosis, and tissue remodeling in AD and add to the existing clinical evidence of its modulation in other Type 2 inflammatory diseases such as EoE.
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Table. Least square mean percent change from baseline in key serum biomarker levels at week 16 in patients with AD

Disclosure

ESD served as a consultant for Abbott, Abbvie, Adare/Ellodi, Aimmune, Akesobio, Alfasigma, ALK, Allakos, Amgen, Aqilion, Arena/Pfizer, Aslan, AstraZeneca, Avir, Biorasi, Calypso, Celgene/Receptos/ Bristol Myers Squibb, Celldex, Eli Lilly, EsoCap, Eupraxia, Dr. Falk Pharma, Ferring, GlaxoSmithKline, Gossamer Bio, Holoclara, Invea, Knightpoint, Landos, LucidDx, Morphic, Nexstone Immunology/Uniquity, Nutricia, Parexel/Calyx, Phathom, Regeneron, Revolo, Robarts/Alimentiv, Salix, Sanofi, Shire/Takeda, Target RWE, Upstream Bio, received research funding from Adare/Ellodi, Allakos, Arena/Pfizer, AstraZeneca, Eupraxia, Ferring, GSK, Meritage, Miraca, Nutricia, Celgene/Receptos/ Bristol Myers Squibb, Regeneron, Revolo, Shire/Takeda, and received educational grant from Allakos, Aqilion, Holoclara, and Invea. JL, SH, LG, CDO, and CC are employees and stockholders of Bristol Myers Squibb. JS served as consultant for Abbvie, Alamar, Aldena, Amgen, AObiome, Apollo, Arcutis, Arena, Asana, Aslan, Attovia, BioMX, Biosion, Bodewell, Boehringer-Ingelheim, Bristol Myers Squibb, Cara, Castle Biosciences, Celgene, Connect Biopharma, Corevitas, Dermavant, Eli Lilly, FIDE, Galderma, GlaxoSmithKline, Incyte, Invea, Kiniksa, Leo Pharma, Merck, My-Or Diagnostics, Nektar, Novartis, Optum, Pfizer, RAPT, Recludix, Regeneron, Sandoz, Sanofi-Genzyme, Shaperon, TARGET-RWE, Teva, Union, UpToDate, participated on a data safety monitoring board or advisory board for Biosion and Sanofi, received grants or contracts from Galderma, Incyte, Pfizer, received honoraria from Abbvie, Eli Lilly, Leo Pharma, Pfizer, Regeneron, Sanofi-Genzyme.

Introduction

The depth of early gastric cancer is an important factor in determining the treatment plan such as endoscopic resection or surgery. We developed an artificial intelligence (AI) program using deep neural networks to diagnose mucosal and submucosal cancers using endoscopic images. Performance evaluation tests were conducted for clinical applications.

Aims & Methods

A validation dataset was established comprising 204 patients with early gastric cancer who underwent endoscopic resection or surgery at our hospital during 2018–2021. The lesion sites were the upper part in 16 (8%), middle part in 106 (52%), and lower part in 82 (40%) patients. The median lesion size (range) was 14 (2–90) mm; the lesion depth was mucosal cancer in 136 (67%) and submucosal cancer in 68 (33%) patients. Five white-light images per case at a minimum were used (median: 8, range: 5–22). The primary endpoint was sensitivity of mucosal cancer, defined as the lower limit of the 95% confidence interval (CI) > 75%, based on previous reports on the depth diagnosis of early gastric cancer. The secondary endpoints were specificity and accuracy.

Results

Sensitivity of this program for the diagnosis of mucosal cancer was 91.2% (95% CI: 85.2%–94.9%), indicating the usefulness of this program. Specificity was 66.2% (95% CI: 54.3%–76.3%), and accuracy was 82.8% (95% CI: 77.1%–87.4%).

Conclusion

The AI program developed for diagnosing the depth of early gastric cancer is useful and expected to be applied clinically in the near future.

Introduction

Potential celiac disease (PCD) is a clinical condition characterized by the presence of a positive CD-specific serology and a normal intestinal architecture. Asymptomatic PCD patients are generally addressed to continue on a gluten containing diet (GCD), but long-term risks of this approach have never been explored.

Aims & Methods

In the present study we aim at investigating nutritional and autoimmune complications possibly developing overtime in a cohort of asymptomatic PCD children left on a GCD. We compared parameters of growth, nutritional status, and autoimmunity between time of diagnosis and in occasion of last medical check, after a long-term gluten containing diet.

Results

Altogether we collected data from 171 PCD children with a mean follow-up time of 3 years (range 0.35-15.3 years). During follow-up, despite patients did not reduce the amount of daily gluten intake , anti-tissue transglutaminase (anti-TG2) antibodies spontaneously and significantly decreased. Most of parameters analysed did not change over follow-up (height centile, ferritin, albumin., cholesterol, calcium, alkaline phosphatase , parathormone, vitamin D, p>0.05) or even improved (weight and BMI centile, haemoglobin, blood iron, HDL, glycaemia and HbA1C, p<0.05), always remaining in the limit of normality. Equally autoantibodies for other concomitant autoimmune disorders did not increase overtime. Similar results were obtained excluding from the analysis patients that stopped producing anti-TG2 and those with a follow up < 3 years.

Conclusion

Our pilot study provides reassuring results on the maintenance of a gluten containing diet in asymptomatic PCD children, even when a long term follow-up is considered.

Disclosure

no conflict of interest to declare

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with the lowest 5-year survival rate of all cancers due to late diagnosis and lack of effective treatment. One of the challenges of developing molecular diagnosis and molecular targeted therapy for PDAC is the “mismatch” between research models and patients.

Aims & Methods

This study aimed i) to identify common proteins among different research models and patients and accordingly ii) to identify potential hub proteins and signaling pathways involved in the PDAC pathogenesis by using proteomics-based system modeling of multimodal datasets derived from multiple research models and patients. Mouse PDAC cells (derived from a genetically engineered mouse model with KrasG12D/Pdx1-Cre), PDAC orthotopically tumors in C57B/L6 mice (n=5), mouse PDAC spheroids, mouse pancreatic organoids, mouse PDAC organoids, normal mouse pancreatic tissues (n=5), human PDAC tumor tissue samples from treatment-naїve (n=20), neoadjuvant chemotherapy (n=22), and human PDAC organoids were included. Mass spectrometry-based proteomics, Rstudio, Cytoscape 3.10 and pathway enrichment analysis (using ingenuity pathways analysis, including human PDAC transcriptomic in addition) were used.

Results

In the comparison of PDAC models and patients, shared proteins were identified as follows: 60% between the mouse PDAC cells and the mouse PDAC spheroids, 78% between the mouse PDAC cells and the mouse PDAC tissue, 56% between the mouse PDAC spheroids and the mouse PDAC tissue, 66% between the mouse PDAC cells and the mouse PDAC organoids, 54% between the mouse PDAC spheroids and the mouse PDAC organoids, 38% between the mouse PDAC and the human PDAC tissues, 31% between the human PDAC tumor and the human PDAC organoids, and 60% between the mouse and the human PDAC organoids. A total of 1,367 common proteins were identified within all the PDAC models. Among these proteins ACTB, HSP90AA1, HSPA8, HSP90AB1, HSPA4, EEF2, RPS3, RPS20 and GAPDH were the top 10 hub proteins (with >300 node connections). Upon comparison of all eight models, eight proteins were identified as unique to PDAC, namely FAHD1, ARSA, NEK9, SERPINB9, LAMTOR2, SNRPG, TMED3, and RAB1A. There was a correlation in terms of z-score regarding signaling pathways between the mouse PDAC and human PDAC (p<0.001), particularly in activations of nerve growth factor signaling, integrin signaling, and neuregulin signaling. Central hub proteins commonly found in the mouse models included vinculin (membrane-cytoskeletal protein) as one of the high connectivity nodes and β-catenin and probable ubiquitin carboxyl-terminal hydrolase FAF-X (involved in axonal growth and neuronal cell migration) as high centrality nodes. In patients, central hub proteins that were dysregulated after chemotherapy included 14-3-3 protein zeta/delta (encoded by YWHAZ gene), β-catenin and SRC. Among the 47 proteins that were associated with altered metabolism after the chemotherapy in patients, CD44, CTNND1, APOA 1, PARVA, and SCRBS1 were nodes with low degrees, and desmosomes (DSP, KP3, PPL), PRTN3, thrombospondin-2 (THBS2), and DMBT1 were the most down-regulated proteins without nodes, and APOA1, caveolin-1 (encoded by CAV1), LRP1 were the most up-regulated proteins with nodes.

Conclusion

The system modeling identified the common proteins among the research models and patients and the hub proteins and signaling pathways, suggesting the potential translational targets of PDAC.

Introduction

Dysregulated T-cell immune response-mediated inflammation is implicated in various liver diseases, yet mechanisms governing liver immune homeostasis control and therapies for limiting T-cell overactivation remain unclear. This study aims to elucidate the role of immunoregulatory myeloid cells (MCregs) in liver injury and their potential to modulate CD4⁺ T-cell hyperactivation via extracellular vesicle (EV)-mediated metabolic regulation.

Aims & Methods

Concanavalin A (ConA) mice and autoimmune hepatitis (AIH) patients were examined for alterations in purine metabolism associated with liver injury severity and therapeutic response. The expression of extracellular purine catabolism nucleases, such as CD39 and CD73, on CD11b⁺Gr-1⁺ myeloid cells (MCs) was assessed, with particular focus on liver injury conditions. The study investigated the natural expansion of the CD39⁺CD73⁺Gr-1^highCD11b⁺ regulatory MC (MCreg) subset during early liver injury phases and its effects on CD4⁺ T-cell hyperactivation and liver injury through in vitro and in vivo experiments. Mechanistic insights into MCreg-mediated immunosuppression were explored, particularly regarding the release of CD73^high EVs and their impact on extracellular AMP conversion, cAMP pathway activation, and inhibition of glycolysis and cytokine secretion in activated CD4⁺ T cells.

Results

Alterations in purine metabolism were observed in ConA mice and AIH patients, correlating with liver injury severity and therapeutic response. CD39 and CD73 expression was markedly upregulated on CD11b⁺Gr-1⁺ myeloid cells under liver injury conditions. The CD39⁺CD73⁺Gr-1^highCD11b⁺ MCreg subset expanded naturally during early-stage liver injury and demonstrated efficacy in suppressing CD4⁺ T-cell hyperactivation and liver injury both in vitro and in vivo. The release of CD73^high EVs by MCregs facilitated extracellular AMP conversion to immunosuppressive metabolites (e.g., adenosine and inosine), activating the cAMP pathway and inhibiting glycolysis and cytokine secretion in activated CD4⁺ T cells.

Conclusion

These findings shed light on the mechanisms governing immune homeostasis during early liver injury and suggest that MCreg or MCreg-EV therapy may offer a targeted approach for preventing diverse liver diseases induced by T-cell overactivation.

Clinical Case Summary

Diaphragmatic flutter (DF) is a rare condition characterized by rhythmic, repetitive, and involuntary contractions of the diaphragm and other respiratory muscles, with variable clinical presentation including epigastric pulsations, sleep disturbances, dyspnea, hyperventilation, hiccups, abdominal pain, gastroesophageal reflux and belching, often leading to delays in diagnosis and ineffective treatments. Diagnostic tools were previously limited to thoracic videofluoroscopy and electromyography.
Patient 1: A 22yr male, with abdominal spasms, constant nausea and reflux during adolescence with poor response to omeprazole.Endoscopy and gastric emptying scintigraphy did not reveal the root of the problem. The condition was intermittent, and psychological evaluation was normal. Due to disabling symptoms, high-resolution esophageal manometry (HREM) was performed, revealing absent motility and additionally observing abdominal spasms associated with oscillating increases in intragastric pressure, pharyngeal contraction and repetitive diaphragmatic spasms. Biofeedback therapy (BT) was conducted with symptomatic control.
Patient 2: A 47yr, male with a 10-year history of abdominal spasms, evaluated by neurology, which ruled out structural abnormalities via cranial magnetic resonance imaging and electromyography. He received multiple medications without symptomatic improvement. The condition was intermittent, with periods of remission. Upon symptom recurrence HREM was performed, showing normal motility but a pattern similar to the previous case. BT achieved optimal symptom control.
Discussion: DF is a rare entity. Cases reported in the literature are generally initially managed by neurologists, with minimal involvement from gastroenterologists. However, it is proposed that HREM assessment, which demonstrates a specific pattern, can facilitate diagnosis without the need for invasive studies. Likewise, BT appears to be effective in the short term, improving patients' quality of life.

References

Ramirez JD, Gonzales M, Hoyos JA, Grisales L. Diaphragmatic flutter: a case report and literature review. Neurología. 2015;30(4): 249-251. 2

Iliceto G, Thompson PD, Day BL, et al. Diaphragmatic flutter, the moving umbilicus syndrome, and "belly dancer's" dyskinesia. Mov Disord 1990;5:15–22

Disclosure

Nothing to disclose

Introduction

Rectal cancer represent 30% of colorectal cancer with 46,220 new cases estimated in the United States in 2024. About 98% of rectal cancer patients have rectal adenocarcinoma. Gastrointestinal (GI) squamous cell neoplasm (SCN) is very rare with no enough studies to estimate its incidence and clinical features. Thus, our aim was to assess the risk of developing second primary GI SCN following chemo(radio)therapy after rectal adenocarcinoma.

Aims & Methods

We used the Surveillance Epidemiology and End Results (SEER) database to obtain the data of 145,789 patients with first primary rectal adenocarcinoma. We used MP-SIR session to calculate the standardized incidence ratios (SIR) ratio as (observed/Expected) with a latency exclusion period of 0 months, Excess risk (ER) was calculated per 10.000 and 95% Confidence Interval (CI). The results were significant if P> 0.05.

Results

Out of 145.789 patients with rectal adenocarcinoma, 4333 patients developed second primary SCN. Among who developed second primary GI SCN, 21.9% received chemotherapy with an O/E of 4.07 (P˂0.05, CI: 4.50 - 3.67, ER=74.53) while 3.25% received radiotherapy with an O/E of 2.23 (P ˂ 0.05, CI: 2.60- CI: 1.90, ER=42.59). The group who had no systemic therapy had an O/E of 2.83 (P˂ 0.05, 95%CI: 2.93–2.74, ER=57.27). The group who received combined chemoradiotherapy had a 2-fold increased risk of developing second primary GI SCN (O/E =2.34, P˂ 0.05, CI:2.44- 2.24, ER=34.88). There was a significant increase in the 0-11 months interval in the four groups. The group who received chemotherapy O/E was 11.73 (P ˂ 0.05, 95 % CI: 13.27– 10.32, ER=233.48) and the group who received radiotherapy had increased O/E to 5.16 (P˂0.05, ER=150.85, 95%CI: 6.51–4.03).

Conclusion

Chemotherapy increases the risk of multiple primary GI SCN after the first primary rectal adenocarcinoma, especially in the first year after diagnosis. Chemo(radio)therapy have a significantly high risk second primary GI SCN with different potential and variations across different intervals. Thus, more attention to be given for the screening of second primary GI malignancies after chemo(radio)therapy administration.