Yan Sun

Neutrophil extracellular traps (NETs) contribute to colorectal cancer (CRC) progression, but the underlying regulatory mechanism remains unknown. This study explored how migration and invasion inhibitory protein (MIIP) influences CRC progression by regulating tumor‐associated neutrophils (TANs) infiltration and NETs formation. On the basis of bioinformatics analysis and clinical CRC samples, we observed that a high density of neutrophils and a high abundance of NETs were correlated with low MIIP expression in CRC tissue as well as distant metastasis and poor prognosis. In vitro co‐culture assays revealed that conditioned medium from CRC cells with downregulated MIIP expression recruited neutrophils to form NETs and that NETs further enhanced the migratory and invasive abilities of CRC cells. Mechanistically, the results of this study demonstrated that MIIP directly bound to Protein Kinase R, which inhibited the Nuclear Factor kappa‐B pathway and Interleukin ‐ 8 expression and accordingly suppressed TANs infiltration and NETs formation. In addition, preliminary results from an intrasplenic mouse model of CRC liver metastasis suggested the potential therapeutic significance of Deoxyribonuclease I (DNase I, a NET inhibitor), and MIIP augmented the inhibition of CRC metastasis by DNase I. This study revealed that MIIP is a regulator of CRC cell‐neutrophil crosstalk and indicated that NETs are potential therapeutic targets in CRC.

Background: Accurate oncologic pathology diagnosis remains cognitively demanding, requiring synthesis of heterogeneous evidence within complex guidelines. Although artificial intelligence has shown promise in pathology, most existing systems are image-centric, whereas large language model (LLM)-based approaches for report-level diagnosis remain limited by hallucination, weak guideline grounding, and insufficient entity-level reasoning. Methods: We developed SAGE-Path (Self-reflective Agentic Guideline-grounded Engine for text-based pathology diagnostic assistance), a framework that integrates retrieval-augmented generation with iterative self-reflective reasoning. The system was grounded in a WHO-derived pathology knowledge base comprising 1,811 tumour entities organized into 48,396 retrievable text units. SAGE-Path was evaluated across multiple pathology tasks, including TNM staging, tumour regression grading, rare-tumour diagnosis, metastatic primary-site inference, and complex diagnostic reasoning. Performance was compared with LLM-only baselines and pathologists. Assisted-reader studies were conducted to assess clinical utility. Findings: SAGE-Path achieved 90.3% accuracy in TNM staging and 99.1% accuracy in tumour regression grading, surpassing junior and senior pathologists in these tasks. In rare-tumour diagnosis and metastatic primary-site inference, SAGE-Path improved diagnostic accuracy by approximately 5–6 percentage points compared with LLM-only baselines and reduced diagnostic errors by about 40%. Mechanistically, retrieval and reflection were complementary: together they eliminated correct-to-error transitions observed with reflection alone, producing evidence-grounded reasoning and actionable recommendations. In assisted-reader evaluations, SAGE-Path improved pathologist performance and reduced completion time. Interpretation: By combining authoritative guideline retrieval with structured self-reflective reasoning, SAGE-Path enables reliable and auditable report-level diagnostic reasoning in oncologic pathology. This framework extends computational pathology beyond image-centric analysis and provides a practical approach for supporting complex diagnostic decision-making in routine pathology practice.

Table S1. The detailed information of genes in Tianjin and MSK-IMPACT panels

Table S5. The comparison of clinicopathologic characteristics of WT-GISTs between Tianjin and MSK-IMPACT cohorts

Table S6. The clinicopathologic parameters and molecular variations of RAS-related GISTs in Tianjin cohort and MSK-IMPACT cohort

The driver genes of wild-type gastrointestinal stromal tumors (WT-GIST), particularly quadruple WT-GISTs (qWT-GIST), remain unclear. In this study, we collected 119 WT-GISTs from two cohorts and analyzed their clinicopathologic and genomic features, particularly for qWT-GISTs. Next-generation sequencing (NGS) revealed several fusion genes and gene mutations, such as ARID1B, SETD2, and PLCG2, in qWT-GISTs. Further integrated Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed significantly enriched signaling pathways in qWT-GISTs, including the hypoxia-inducible factor-1 (HIF-1). For qWT-GISTs, large tumors or a high mitotic index prompted a shorter recurrence-free survival (RFS) and a high mitotic index or involvement of the HIF-1 pathway prompted a shorter overall survival (OS); however, neither RFS nor OS was prolonged by postoperative adjuvant therapy. In addition, compared with succinate dehydrogenase complex (SDH)-deficient GISTs, qWT-GISTs were less frequently found in the stomach and less frequently presented as high mitotic index; compared with RAS-related GISTs, qWT-GISTs were more frequently found in the stomach. Stratified analyses showed, in patients with low recurrence risk, qWT-GISTs had better RFS than SDH-deficient GISTs. In patients with high recurrence risk or with postoperative adjuvant therapy, qWT-GISTs presented worse OS than SDH-deficient GISTs. In summary, qWT-GISTs exhibited unique clinicopathologic characteristics and outcomes compared with SDH-deficient and RAS-related GISTs, suggesting that they should be managed using different treatment and follow-up strategies, especially stratified management. Considering the rarity and heterogeneity of WT-GISTs, a regulatory detection procedure should be established for WT-GISTs, including NGS for qWT-GISTs, to identify the molecular mechanisms and potential therapeutic targets.Implications:WT-GISTs are heterogenous tumors which should be managed using different treatments and follow-up strategies.

Table S4. The clinicopathologic parameters and molecular variations of SDH-deficient GISTs in Tianjin cohort and MSK-IMPACT cohort

Table S3. The clinicopathologic characteristics and comparisons of WT-GISTs from Tianjin and MSK-IMPACT cohorts

Table S7. The clinicopathologic parameters of qWT-GIST in Tianjin cohort and MSK-IMPACT cohort

Figure S1. The overlapped and non-overlapped genes among Tianjin and MSK-IMPACT panels.