Introduction

Esophageal squamous cell carcinoma (ESCC) is associated with poor prognosis despite advances in neoadjuvant immunochemotherapy (NICT). With highly variable treatment responses, identifying reliable predictive biomarkers remains crucial. While gut microbiome influences on immunotherapy efficacy are increasingly recognized, the specific role of the fungal microbiome (mycobiome) in NICT outcomes for ESCC remains unexplored, representing a significant gap in developing personalized treatment strategies.

Aims & Methods

This study aimed to characterize gut mycobiome signatures in ESCC patients and develop fungal biomarker-based predictive models for NICT response. We conducted ITS2 sequencing on 136 fecal samples collected from 68 ESCC patients (pre- and post-NICT) and 19 healthy controls (HC). Patients were stratified as responders (R, n=26) or non-responders (NR, n=42) based on tumor regression grade (TRG) scores. We performed comprehensive analyses of mycobiome diversity, composition, co-occurrence networks, and functional pathways across groups. The Boruta algorithm was employed for feature selection to identify fungal biomarkers, followed by multilayer perceptron (MLP) machine learning with five-fold cross-validation to develop predictive models. Additionally, ESCC xenograft models were established to investigate gut mycobiome-immune interactions through fecal microbiota transplantation (FMT) and targeted fungal manipulation experiments.

Results

ESCC patients demonstrated significant mycobiome dysbiosis compared to healthy controls, characterized by reduced fungal alpha diversity and distinct beta diversity patterns. At baseline, ESCC patients showed enrichment of Rhodotorula minuta, Actinomucor elegans, and several Candida species. Responders exhibited significantly higher baseline fungal diversity than non-responders, with enrichment of Meyerozyma, Candida boidinii, Trichosporon dermatis, and Cryptococcus species, whereas non-responders showed predominance of Saccharomyces, Nakaseomyces, and pathogenic Candida species (C. glabrata, C. parapsilosis, C. albicans). Responders also displayed more complex, stable fungal co-occurrence networks with higher density and clustering coefficients. Post-NICT, both groups showed partial recovery of mycobiome diversity, with significant increases in beneficial fungal taxa. Functional pathway analysis revealed distinct metabolic profiles between responders and non-responders. Using the Boruta algorithm, we identified 23 genus-level and 22 species-level fungal biomarkers that demonstrated high predictive value. The MLP model based on genus-level markers achieved AUCs of 90.2% (training) and 91.9% (test), while the species-level model showed comparable performance. In animal experiments, mice receiving R-FMT exhibited reduced tumor growth and enhanced CD8+ T cell infiltration compared to NR-FMT recipients. Notably, Candida boidinii supplementation synergized with anti-PD-1 therapy, while Saccharomyces depletion enhanced treatment sensitivity.

Conclusion

Our findings establish the gut mycobiome as a powerful predictor of NICT response in ESCC, with machine learning models achieving >90% accuracy using fungal biomarkers. Experimental validation not only confirms mechanistic links between specific fungi and antitumor immunity but also demonstrates that targeted mycobiome modulation can enhance immunotherapy efficacy. This dual approach of prediction and intervention offers a novel framework for precision oncology in ESCC, potentially transforming clinical decision-making and treatment outcomes in this challenging malignancy.

References

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Introduction

Gastric mucosal visibility is essential for high-quality upper gastrointestinal (GI) endoscopy. While simethicone and N-acetylcysteine (NAC) have been shown to improve mucosal visibility, the optimal time interval between preparation and endoscopy remains undefined.

Aims & Methods

This randomized controlled trial evaluated the effect of timing on mucosal visibility and explored the upper limit beyond which visibility begins to decline.
In this single-center, randomized controlled trial, adult patients undergoing elective upper GI endoscopy were randomized into four groups: placebo, simethicone + NAC with endoscopy performed after 10–20 minutes, 20–30 minutes, and >30 minutes. The primary outcome was the proportion of patients achieving adequate gastric mucosal visibility (GMV), defined as a score of <7 on a validated visibility scale. A pre-specified subgroup analysis of the >30 minute group was performed by stratifying patients into 20-minute intervals (31–50, 51–70, and 71–90 minutes) to assess the durability of mucosal clarity over time.

Results

Comparison	Adequate gastric mucosal visibility (%)	P
10–20 min vs 20–30 min	27% vs 62%	0.001
10–20 min vs >30 min	27% vs 67%	0.001
10–20 min vs Placebo	27% vs 2.6%	0.001
20–30 min vs >30 min	62% vs 67%	0.174
20–30 min vs Placebo	62% vs 2.6%	0.001

1,140 patients (median age 42years, 63.2% males) were included in the study. Adequate GMV was significantly higher in all preparation groups compared to placebo (p < 0.001). The 20–30 minute and >30 minute groups demonstrated the highest rates of adequate visibility (61.5% and 73.5%, respectively), both significantly superior to the 10–20 minute group (26.9%) and placebo (2.6%). In the >30 minute subgroup analysis, the 31–50 minute interval demonstrated the highest adequacy (73.5%). The 51–70 minute group showed moderate adequacy (52.1%), which was not significantly different from the 20–30 minute group (p = 0.207). However, the 71–90 minute group had reduced adequacy (43.8%) and did not differ significantly from the 10–20 minute group (p = 0.145). No adverse events were reported across study arms.

Conclusion

Endoscopy performed between 20 and 50 minutes after mucosal preparation yields optimal gastric mucosal visibility. A decline in visibility beyond 50 minutes suggests a practical upper limit for endoscopy timing following preparation. These findings support structured, time-sensitive scheduling following mucosal cleansing to enhance procedural quality.

Disclosure

None