Clinical trials

In a retrospective analysis of 3,054 advanced NSCLC patients, 41 with EGFR exon 19 deletion-insertions (19delins) received first-generation EGFR TKIs, achieving median PFS of 10.4 months; those with L747\_T751delinsP had notably longer PFS of 18.7 months. Another study of 2,467 treatment-naïve patients found 93 with 19delins treated with first-generation TKIs had median PFS of 19 months, exceeding the 13 months for common 19del mutations. For third-generation TKIs, a study of 215 19delins patients (57 first-line) showed median PFS of 12.9 months, inferior to 23.2 months for common 19del. Our center's retrospective study of 4,666 NSCLC patients in Fujian (2017-2020) included 69 with 19delins: median PFS was 16.7 months with first-generation TKIs versus 7.2 months with third-generation. Evidence suggests specific EGFR deletion locations may affect TKI efficacy; first-generation TKIs may be superior in some 19delins subtypes, while third-generation appear limited. Large prospective data are lacking. This project aims to compare first- versus third-generation EGFR TKIs in 19delins patients via a randomized controlled trial, stratify sensitivity by subtype, and improve survival.

SMARCA4 mutation is clinically known to be an independent poor prognostic factor. Both TCGA and the investigators institution's preliminary research indicate that the median overall survival (OS) of mutated patients is significantly shorter than that of wild-type patients (32 months vs. 157.7 months, respectively, P=0.001); it is associated with chemotherapy/immunotherapy resistance, rapid progression, and poor prognosis (OS\<12 months). Current standard first-line treatments (such as platinum-based chemotherapy ± immunotherapy) have limited efficacy in SMARCA4-mutated patients (ORR\<30%, PFS\<4 months). Additionally, NapsinA-positive expression can improve the survival of mutated patients (median OS: 32 months vs. 15 months, P=0.033), but this effect exists only in the SMARCA4-mutated group. The ABCP regimen has a synergistic mechanism: Atezolizumab (anti-PD-L1): reverses the immunosuppressive microenvironment; Bevacizumab (anti-VEGF): inhibits angiogenesis and enhances T-cell infiltration; Platinum + Paclitaxel: directly kill tumor cells and release neoantigens. Therefore, the investigators preset that SMARCA4 mutation may affect chemotherapy/immunotherapy response through epigenetic regulation, and its value as a predictive biomarker needs to be validated. Hence, the purpose of this study is to observe and explore the efficacy and safety of first-line ABCP four-drug combination therapy in patients with advanced SMARCA4-mutated non-small cell lung cancer.

Lung cancer can be divided into two major categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for about 85% and SCLC about 15%. The prognoses of different types of lung cancer vary significantly. Early identification of different pathological types of lung cancer is crucial to the patient's prognosis. Raman Spectrum (RS), as a non-invasive and highly specific molecular detection technique, can obtain information at the molecular level, thereby sensitively detecting changes in biomolecules related to tumor metabolism such as proteins, nucleic acids, lipids, and sugars. Surface-enhanced Raman spectroscopy (SERS), developed based on this technology, is one of the feasible methods for high-sensitivity biomolecular analysis. In preliminary study, the investigators collected serum Raman spectral data from a cohort of 233 patients with malignant lung tumors and built a Raman intelligent diagnostic system for SCLC and NSCLC based on a machine learning model, achieving an accuracy rate of 80%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical study aims to validate the use of this intelligent diagnostic system for the early diagnosis of SCLC.

Summary:This study is a prospective, multicenter clinical study. In previous studies, we successfully constructed a CHA reaction-mediated self-calibrated SERS biosensor for the detection of EGFR mutation typing (Del-19, T790M, L858R) in lung cancer patients, and verified that the accuracy, sensitivity, and specificity of the SERS biosensor exceeded 95% in a small sample of 32 patients. In order to obtain the highest level of clinical evidence and truly achieve clinical transformation, this prospective, multicenter clinical study aims to verify the analytical efficiency of the SERS biosensor for EGFR mutation typing in patients with advanced lung cancer. Purpose：This prospective, multicenter clinical study aims to verify the analytical efficacy of the previously constructed CHA reaction-mediated self-calibrated SERS biosensor in EGFR mutation typing in patients with advanced lung cancer. Research subjects: The patients enrolled in this project are confirmed to be advanced non-small cell lung cancer (NSCLC). Enrollment will be completed in 25 centers and the enrollment will be competitive. Research location: 900th Hospital of Joint Logistics Support Force Research intervention: None Study duration: Patients will be enrolled from June 2024 to June 2025. Subject participation time: Telephone follow-up will be conducted every three months until the end of the study.

Pulmonary nodules are often an early indicator of lung cancer. With the widespread adoption of chest CT scans in routine physical examinations, an increasing number of pulmonary nodules are being detected, including a variety of small nodules such as inflammatory lesions, benign tumors, and malignant tumors. Currently, there is no unified international consensus on the diagnostic and treatment strategies for pulmonary nodules, as outlined by various global guidelines. Developing and implementing a comprehensive lung nodule and lung cancer screening program within public health management systems remains a complex and challenging endeavor. Advancing research and proposing lung cancer screening technologies that are highly sensitive, highly specific, simple, accessible, and cost-effective is an essential and pressing priority in modern healthcare. Raman spectroscopy (RS), as a non-invasive and highly specific molecular detection technique, can be obtained at the molecular level to sensitively detect changes in biomolecules composed of proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples. The surface enhanced Raman spectroscopy (SERS) developed based on this technology is one of the feasible methods for high-sensitivity biomolecule analysis. Although SERS technology has shown good diagnostic efficacy in lots of preclinical studies in multiple tumors, it is limited to a generally small sample size and lacks external validation. There for, a clinical study of Raman spectra for tumor diagnosis is needed, which meets the following requirements: 1.An objective, fast and practical application of Raman spectral data processing is needed and deep learning method may be the best classification method; 2. It requires multicenter and large clinical samples to train deep learning diagnostic model, and verify its true efficacy through external data of prospective study. In preliminary research, the investigators collected serum Raman spectroscopy data from a cohort of 191 patients with pulmonary nodules and developed an intelligent diagnosis system for distinguishing between benign and malignant pulmonary nodules using a machine learning model. The system achieved an accuracy of 89.7%. In order to obtain the highest level of clinical evidence and truly realize clinical transformation, this prospective, multi-center clinical study is designed to verify the intelligent diagnostic system for early diagnosis of prostate cancer.

Surgery is the main treatment for early lung cancer. It is worth noting that there are significant differences in postoperative prognosis and surgical methods between microinvasive cancer and early-stage invasive cancer. Micro invasive lung cancer can achieve 100% long-term survival through surgical resection, without the need for postoperative adjuvant radiotherapy. There is no need to remove lung lobes during surgery, only segmental or wedge resection is required, and systematic lymph node dissection is not recommended. Therefore, accurate prediction of preoperative and intraoperative microinvasive cancer and invasive cancer in pulmonary nodules is crucial for patients to choose surgical methods, which can significantly affect postoperative lung function retention and overall survival. Raman spectroscopy (RS), as a non-invasive and highly specific molecular detection technique, can be obtained at the molecular level to sensitively detect changes in biomolecules composed of proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples. The surface enhanced Raman spectroscopy (SERS) developed based on this technology is one of the feasible methods for high-sensitivity biomolecule analysis. We collected serum Raman spectroscopy data from a cohort of 138 early lung cancer patients in our preliminary research. Based on a machine learning model, we constructed an early lung microinvasive cancer and invasive cancer Raman intelligent diagnosis system, which achieved an accuracy rate of 89.4%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical study aims to validate the use of this intelligent diagnostic system for early diagnosis of lung cancer and the discrimination between microinvasive cancer and invasive cancer.

Although modern medicine has made significant progress in the diagnosis and treatment of lung cancer, most patients are diagnosed at locally advanced stage or with distant metastases, especially in the late stages where the cancer has spread to other organs through hematogenous metastasis. This not only significantly the survival rate of patients but also increases the complexity and difficulty of treatment. Hematogenous metastasis plays an important role in the clinical progression of lung cancer, its complex biological processes pose a huge challenge for clinical management. Early detection of hematogenous metastasis is difficult, and traditional imaging methods have limited sensitivity in detecting small metastatic lesions. The emerging technology of circulating tumor cells (CTCs) has been limited in clinical application due to its high detection costs and technical requirements. Therefore researching and developing high-sensitivity, high-specificity, simple, easy-to-popularize, and low-cost technologies to predict the risk of hematogenous metastasis lung cancer is crucial for early diagnosis and more precise treatment. Raman spectroscopy (RS), a non-invasive and highly specific molecular detection technology, can detect in biomolecules such as proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples at the molecular level. Surface-enhanced R spectroscopy (SERS), developed based on this technology, is one of the feasible methods for high-sensitivity biomolecular analysis. Although SERS technology has shown diagnostic results in numerous preclinical studies of various tumors, it is limited by small sample sizes and lacks external validation. Therefore, clinical studies on the diagnosis of tumors Raman spectroscopy are needed, with the following requirements: 1. Objective, rapid, and practical Raman spectroscopy data processing methods are needed, and and deep learning methods may be the best classification methods; 2. Multicenter, large-sample clinical samples are needed to train deep learning diagnostic models, and real-world performance should be validated through external data from prospective studies. In previous study, the investigators collected serum Raman spectroscopy data from a cohort of 23 patients with lung malignancies and developed an intelligent Raman diagnostic system for hematogenous metastasis in non-small cell lung cancer (NSCLC) based on learning models, with an accuracy rate of 95%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical aims to validate the use of this intelligent diagnostic system for early diagnosis of hematogenous metastasis in NSCLC.

To explore the efficacy of Tislelizumab combined with chemotherapy in the treatment of locally unresectable esophageal squamous cell carcinoma (ESCC)