Introduction

Immune checkpoint inhibitors (ICIs) have marked a breakthrough in the management of a wide range of cancer types, leading to increased overall survival. However, this treatment is frequently associated with the onset of various immune-related adverse events (irAEs), including ICI-related diarrhea and colitis.¹ Current recommendations for patients with diarrhea or colitis grade 2 or higher (graded via CTCAE)² include the discontinuation of ICI and administration of immune suppressants (e.g. steroids or biologics), which may lead to worse outcomes in these patients.³ Therefore, alternative therapeutic strategies to manage irAEs are needed. The gut microbiota plays a pivotal role in the onset of gastrointestinal irAEs, but the role of its therapeutic modulation on these disorders is mostly unknown. ^4,5

Aims & Methods

Our aim was to evaluate the efficacy and safety of therapeutic microbiome modulation for gastrointestinal irAEs in cancer patients. We enrolled cancer patients affected by ICI-related diarrhea/colitis referred to our Microbiome Clinic from January 2023 to March 2025. After clinical evaluation, patients were stratified according to severity of diarrhea (graded via CTCAE)² and then received a microbiome-modulating therapy. Patients were followed-up for at least 8 week after the initiation of therapy. We collected data about patient demographics (age and gender), patient’s overall health status as assessed by the the Charlson Comorbidity Index (CCI)⁶, type of cancer, oncological therapy and its suspension, time in weeks from initiation of cancer therapy to onset of diarrhea, severity of diarrhea (graded via CTCAE)², time in weeks until resolution of diarrhea and resumption of oncological therapy

Results

We enrolled 42 patients in the study period (8 males, 34 females, mean age 58,3 years-old, mean CCI-index 5.8). All patients were treated with ICI (Pembrolizumab: n=25 , Dostarlimab: n=4, Nivolumab: n=4; Atezolizumab: n=3; Novulumab and Ipilumab: n=2, Cemiplimab: n=2; Mogamulizumab: n=1; Pertuzumab: n=1). Ten patients experienced G1 diarrhea (23,8%), 16 (38,1) G2, 15 (35,7%) G3 and one G4 (2,4%). As indicated by current European Guidelines³, 30 patients (71,4%) had to interrupt ICI due to diarrhea. Multi-strain probiotics were administered to 27 (64.3% ) patients, rifaximin to 22 (52,4%). Overall, 21 patients (50%) needed therapy with systemic steroids. Seven patients who experienced ICI-related colitis refractory to steroids and biologics were successfully treated with FMT. Twenty-eight patients (66.7% ) had a resolution of the diarrhea (G0) and 11 patients (26.2%) had diarrhea G1 at a mean time of 8 weeks. Upon resolution of diarrhea, nine of the 30 patients who had interrupted ICI (30%) were able to resume the treatment. None of the patients experienced any adverse event related to gut microbiota modulation as defined by CTCAE².

Conclusion

Therapeutic modulation of gut microbiota appears to be a promising safe and effective approach for treating ICI-related diarrhea and colitis. However, these preliminary findings need confirmation through more consolidated and well-designed studies.

References

1. Dougan M, Wang Y, Rubio-Tapia A, Lim JK. AGA Clinical Practice Update on Diagnosis and Management of Immune Checkpoint Inhibitor Colitis and Hepatitis: Expert Review. Gastroenterology. 2021;160(4):1384-1393. doi:10.1053/j.gastro.2020.08.063
2. Freites-Martinez A, Santana N, Arias-Santiago S, Viera A. Using the Common Terminology Criteria for Adverse Events (CTCAE - Version 5.0) to Evaluate the Severity of Adverse Events of Anticancer Therapies. Actas Dermosifiliogr. 2021;112(1):90-92. doi:10.1016/j.ad.2019.05.009
3. Haanen J, Obeid M, Spain L, et al. Management of toxicities from immunotherapy: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2022;33(12):1217-1238. doi:10.1016/j.annonc.2022.10.001
4. Wang Y, Wiesnoski DH, Helmink BA, et al. Fecal microbiota transplantation for refractory immune checkpoint inhibitor-associated colitis. Nat Med. 2018;24(12):1804-1808. doi:10.1038/s41591-018-0238-9
5. Wang T, Zheng N, Luo Q, et al. Probiotics Lactobacillus reuteri Abrogates Immune Checkpoint Blockade-Associated Colitis by Inhibiting Group 3 Innate Lymphoid Cells. Front Immunol. 2019;10. doi:10.3389/fimmu.2019.01235
6. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis. 1987;40(5):373-383. doi:10.1016/0021-9681(87)90171-8

Disclosure

G.I. has received personal fees for acting as speaker for Biocodex, Danone, Sofar, Malesci, Metagenics and Tillotts Pharma, and for acting as consultant and/or advisor for Ferring Therapeutics, Giuliani, Malesci and Tillotts Pharma. A.G. reports personal fees for consultancy from Eisai Srl, 3PSolutions, Real Time Meeting, Fondazione Istituto Danone, SinergieSrl, Board MRGE and Sanofi SpA personal fees for acting as a speaker for Takeda SpA, AbbVie and Sandoz SpA and personal fees for acting on advisory boards for VSL3 and Eisai. G.C. has received personal fees for acting as advisor for Ferring Therapeutics. All other authors have no conflicts of interest to disclose.

Introduction

Corpus atrophic gastritis (CAG) requires endoscopic-histological surveillance due to the risk of developing gastric neoplastic lesions (GNL). This study aimed to identify variables associated with GNL development at long-term follow-up using a feature ranking method based on a novel One-Class Support Vector Machine (SVM) model.

Aims & Methods

A dataset containing clinical, endoscopic, and histological variables from consecutive CAG patients (2001-2023) adhering to a surveillance program was considered. GNL presence at the longest available follow-up was recorded. Gastric biopsies and histological evaluations were conducted according to the updated Sydney system. CAG patients with any missing data or a GNL diagnosis at the baseline gastroscopy were excluded from the final analysis. A Fisher score-based feature ranking method and One-Class SVM were used to select the optimal subset of baseline variables associated with GNL development, aiming to achieve average sensitivity (Se) and specificity (Sp) above 80%.

Results

Overall, 355 CAG patients were initially considered. Of these, 36 were excluded due to the presence of GNL at baseline gastroscopy, and 216 for missing data. Thus, a total of 103 patients were considered and grouped into: CAG patients with [22 patients (F 68.1%), median age 68 (35-83) years] and without GNL at follow-up [81 patients (F 72.8%), median age 59(26-84) years]. After a median follow-up of 60 (12-192) months, 13 epithelial GNL (gastric adenocarcinoma or high/low-grade dysplasia) and nine type-1 gastric-neuroendocrine-tumors (T1gNET) were recorded. Parietal-cell-antibodies and pepsinogen-I < 30 μg/l were associated with both epithelial GNL and T1gNET. Antral inflammation and age > 60 were linked to epithelial GNL, while anti-thyroperoxidase-antibodies, smoking, and dyspeptic symptoms at CAG diagnosis were linked to T1gNET. Low-dose aspirin and H. pylori eradication therapy showed inverse associations with epithelial GNL and T1gNET, respectively.

Conclusion

This is the first study in which an AI model simultaneously considers clinical, endoscopic, and histological characteristics from a dataset of CAG patients, showing good sensitivity and specificity in identifying those variables associated with developing GNL. Nonetheless, we acknowledge the need for improved variable standardization and data balancing between groups to enhance model performance further.

Introduction

Inflammatory Bowel Disease (IBD)^1,2 requires close monitoring to detect flare ups early and guide treatment. While endoscopy is the gold standard, it is invasive, costly, and uncomfortable^1,2. Intestinal Ultrasound (IUS) is a non-invasive alternative with strong correlation to endoscopic findings—especially bowel wall thickness (BWT), a marker of disease activity³. Nevertheless, IUS remains limited due to operator dependence and a steep learning curve⁴.
AI has improved consistency in other domains, e.g. endoscopic evaluation, but its application to IUS in IBD remains underexplored. Prior models lack interpretability and require manual image cropping, or are trained on ideal, selected data, limiting clinical use^5,6.

Aims & Methods

We aim to develop a deep-learning model that automatically identifies and paints the bowel wall and measures BWT directly from raw clinical IUS images.
A training dataset of 570 images from 144 IUS videos and a testing dataset of 55 images from 55 separate exams were created. All images were extracted from previously performed IUS examinations reflecting real-world variation, including e.g. unclear boundaries and artefacts.
All images were annotated by International Bowel Ultrasound Group (IBUS) certified experts, including outline-paintings of the inner and outer bowel wall and two BWT measurements.
The AI consists of a combination of Convolutional neural networks and other image processing algorithms.
Evaluation included BWT error against the expert mean, classification accuracy using the standard IBUS 3 mm threshold³, and a leave-one-out comparison with individual doctors.

Results

The model produced predictions on 54 of 55 test images.
The model deviated from the gold standard mean by 0.98 mm (SD 1.1 mm) per image on the regression task. The average distance to expert-defined bounds was 0.44 mm (SD 0.89 mm), with 59% of predictions inside this range.
For classification (using a 3 mm threshold), the model reached an accuracy of 0.77, sensitivity of 0.69, specificity of 0.94, and Cohen’s Kappa of 0.56.
In the leave-one-out analysis, expert performance ranged from 0.89–0.93 accuracy and 0.79–0.83 Kappa. Depending on which expert was excluded from the gold standard, the model achieved 0.74–0.80 accuracy and 0.49–0.60 Kappa. Experts stayed within expert-defined bounds in 72–81% of cases, while the model did so in 46–55%.

Conclusion

Clinically, the model performed well. An error of 0.5–1 mm is negligible in practice and matches typical variation in manual measurements. Many real-world test images had BWT values near the 3 mm threshold, so small deviations led to misclassifications. In practice, a 2.5 mm reading could still raise concerns based on symptoms³.
While the system alone cannot provide expert-level BWT measurements, it could provide assistance to experts as well as non-expert and junior doctors, especially in locating the bowel, which is an essential part of IUS.
The model works on unprocessed clinical data, with the only selection criteria being that there is an identifiable bowel segment, measurable by an IBUS-certified doctor. Allowing the images to reflect realistic conditions
In summary, we developed an AI model that identifies the bowel and measures BWT with acceptable accuracy. We have already started the processes of collecting video data, to extend the AI’s functionality to a fully clinical setting.

References

1. Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L. Crohn’s disease. The Lancet. 2017;389(10080):1741-1755. doi:10.1016/S0140-6736(16)31711-1
2. Ungaro R, Mehandru S, Allen PB, Peyrin-Biroulet L, Colombel JF. Ulcerative colitis. Lancet. 2017;389(10080):1756-1770. doi:10.1016/S0140-6736(16)32126-2
3. Novak KL, Group I, Nylund K, et al. Expert Consensus on Optimal Acquisition and Development of the International Bowel Ultrasound Segmental Activity Score [IBUS-SAS]: A Reliability and Inter-rater Variability Study on Intestinal Ultrasonography in Crohn’s Disease. J Crohns Colitis. 2021;15(4):609-616. doi:10.1093/ECCO-JCC/JJAA216
4 Bove L, Meyer J, Collins M, Frampas E, Bourreille A, Le Berre C. Understanding the learning curve of intestinal ultrasound in inflammatory bowel disease: A comparative study between novice, regular, and expert. Clin Res Hepatol Gastroenterol. 2025;49(3):102548. doi:10.1016/J.CLINRE.2025.102548
5. Kumaralingam L, Le May K, Dang VB, Alavi J, Huynh HQ, Le LH. Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images. J Crohns Colitis. 2025;19(4). doi:10.1093/ECCO-JCC/JJAF037
6. Carter D, Albshesh A, Shimon C, et al. Automatized Detection of Crohn’s Disease in Intestinal Ultrasound Using Convolutional Neural Network. Inflamm Bowel Dis. February 2023:1-6. doi:10.1093/IBD/IZAD014

Introduction

Current therapies for inflammatory bowel disease (IBD) suppress inflammation rather than inhibiting underlying microbial drivers. Treatment failures of frontline interventions, including corticosteroids, can lead to dose escalation and risk of adverse effects.^1,2 Bacteria with pro-inflammatory potential, like adherent-invasive Escherichia coli (AIEC), have been postulated as a possible microbial driver in Crohn’s disease (CD), however, targeting these bacteria with precision in IBD remains challenging.^3,4 Most antibiotics have broad-spectrum activity and disturb the background microbiome, which may exacerbate inflammation.⁵ Attention has shifted towards bacteriophages, yet their underlying mechanisms of treating IBD, and how they may work as an adjunctive therapy, remains elusive.

Aims & Methods

We aimed to assess whether and how phage therapy, specific for a CD-associated bacterium, reduces colitis severity using gnotobiotic mouse models.
In vitro kill curves and biofilm challenges were used to identify lytic phage candidates. Adult germ-free C57BL/6 mice were colonized with altered Schaedler-flora (ASF) and E. coli NRG857c (NRG), a CD-associated isolate. After 3 weeks, mice were treated with an NRG-specific lytic phage (1x10⁹ PFU/dose; daily or TIW) or vehicle (PBS) for 2 weeks (n=6/group) and then exposed to 1 cycle of dextran sulfate sodium (2%; DSS) in drinking water. PBS-treated mice (n=6) not exposed to DSS served as controls. Phage treatment was also tested in a spontaneous model of colitis using C57BL/6NTac-Il10^em8Tac (IL-10^-/-) mice, treated weekly. Mice were monitored daily for weight, stool consistency, and occult blood. Fecal contents were cultured to determine bacterial load, and the expression of NRG fimS was quantified using qPCR as an assessment of virulence. At endpoint, colon tissues were collected for histological and immunohistochemistry analysis to quantify colitis severity and NRG infiltration, respectively. Additional C57BL/6 mice were colonized and treated as previously described. During colitis induction, mice received a low-dose of budesonide (2μg/day)⁶ (n=5) or vehicle (n=5) until endpoint. Mice were monitored daily for disease activity and colon tissues were collected for histological analysis. HPLC was used to determine active budesonide levels.

Results

HER259 was identified as a lytic phage against CD-associated AIEC NRG. In ASF and NRG colonized mice, HER259 reduced clinical symptoms (p< 0.001, p< 0.001) and histological scores (p< 0.0001, p< 0.001) in chemical induced and spontaneous colitis, respectively. A 1-log reduction in NRG bacterial load was observed in HER259-treated mice (p< 0.001). Reisolated NRG exhibited a reduced FimS ON/OFF ratio (p-value < 0.01). Immunohistochemistry revealed reduced infiltration of NRG into the lamina propria following HER259 treatment, with greater accumulation in the epithelium. HER259 treatment increased the efficacy of a sub-therapeutic dosing regimen of budesonide (p< 0.001), which was independent of microbial-based drug metabolism as determined by HPLC.

Conclusion

Our results demonstrate that lytic phage targeting a CD-associated bacterium attenuates colitis in two gnotobiotic mouse models. The intervention did not lead to complete eradication of the disease-driving agent, but instead modulated bacterial virulence mechanisms and enhanced responses to corticosteroid therapy. The data suggests that alternative mechanisms should be considered when translating phage therapy to treat IBD in clinical trials.

References

1. Yan, Y. et al. Commensal bacteria promote azathioprine therapy failure in inflammatory bowel disease via decreasing 6-mercaptopurine bioavailability. Cell Rep Med 4, 101153 (2023).
2. Nagayama, M., Gogokhia, L. & Longman, R. S. Precision microbiota therapy for IBD: premise and promise. Gut Microbes 17, 2489067 (2025).
3. Darfeuille-Michaud, A. et al. High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn’s disease. Gastroenterology 127, 412–421 (2004).
4. A, D.-M. et al. Presence of adherent Escherichia coli strains in ileal mucosa of patients with Crohn’s disease. Gastroenterology 115, 1405–1413 (1998).
5. Nelson, R. L., Suda, K. J. & Evans, C. T. Antibiotic treatment for Clostridium difficile-associated diarrhoea in adults. Cochrane Database Syst Rev 3, (2017).
6. Bercik, P. et al. Chronic gastrointestinal inflammation induces anxiety-like behavior and alters central nervous system biochemistry in mice. Gastroenterology 139, (2010).