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Liang Zheng

Publications and source records attributed to Liang Zheng.

2 recordsLinked to original sources
PubMed2026

Randomized phase-II trial of surufatinib plus FOLFOX/FOLFIRI versus FOLFOXIRI as second-line therapy for metastatic colorectal cancer.

BACKGROUND: Second-line treatment for metastatic colorectal cancer (mCRC) typically involves oxaliplatin- or irinotecan-based doublet chemotherapy with or without anti-angiogenic antibodies. Triplet regimens such as FOLFOXIRI have demonstrated synergy and improved efficacy as first-line therapy. Surufatinib, an oral multi-kinase inhibitor targeting VEGFR1-3, FGFR1, and CSF-1R, may enhance chemotherapy efficacy. We evaluated surufatinib combined with doublet (FOLFOX/FOLFIRI) versus triplet (FOLFOXIRI) chemotherapy as second-line treatment for mCRC. PATIENTS AND METHODS: This multicentre, open-label, randomized phase-II trial used Simon's minimax two-stage design. Eligible patients had mCRC progressing on or within 6 months after first-line doublet chemotherapy. Patients were randomized 1:1 to surufatinib 250 mg once daily plus either mFOLFOX6/FOLFIRI (doublet cohort, selected based on prior regimen) or FOLFOXIRI (triplet cohort). The primary endpoint was objective response rate (ORR). RESULTS: From September 2021 to November 2023, 57 patients were randomized (28 per cohort after one withdrawal). In the doublet cohort, ORR was 35.7% (95% CI: 18.6-55.9), median progression-free survival (PFS) was 5.4 months (95% CI: 3.8-7.0), and median overall survival (OS) was 19.0 months (95% CI: 9.2-28.8). In the triplet cohort, ORR was 39.3% (95% CI: 21.5-59.4), median PFS was 5.8 months (95% CI: 3.3-8.2), and median OS was 10.9 months (95% CI: 6.0-15.8). Grade ≥3 treatment-emergent adverse events occurred more frequently in the triplet (71.4%) versus doublet (57.1%) cohort, with higher rates of treatment delays (89.3% versus 72.0%) and discontinuations (25.0% versus 14.3%). CONCLUSIONS: Surufatinib plus doublet chemotherapy showed encouraging antitumor activity and acceptable tolerability in second-line mCRC, warranting further evaluation in a larger randomized trial. In contrast, surufatinib plus triplet chemotherapy was associated with increased toxicity, more frequent treatment delays or discontinuations, and shorter overall survival; this combination is not recommended for further investigation in this setting.ClinicalTrials.gov: NCT04734249Date of registration: January 31, 2021.

Humans↗
PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗