周一“星”视角|基于高灵敏度检测平台的中国食管鳞癌诊断；18F-FAPI PET/CT预测新辅助卡瑞利珠单抗联合化疗的病理反应

01

Yu Wang1, Shan Xing2, Yi-Wei Xu3, Qing-Xia Xu4, Ming-Fang Ji5, Yu-Hui Peng6, Ya-Xian Wu7, Meng Wu8, Ning Xue9, Biao Zhang10, Shang-Hang Xie11, Rui-Dan Zhu12, Xin-Yuan Ou13, Qi Huang14, Bo-Yu Tian15, Hui-Lan Li16, Yu Jiang17, Xiao-Bin Yao18, Jian-Pei Li19, Li Ling20, Su-Mei Cao21, Qian Zhong22, Wan-Li Liu23, Mu-Sheng Zeng24

1 Department of Experimental Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.

2 Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.

3 Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, China.

4 Department of Clinical Laboratory, Affiliated Tumor Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou Key Laboratory of Digestive System Tumor Marker Diagnosis, Zhengzhou, China.

5 Cancer Research Institute of Zhongshan City, Zhongshan City People's Hospital, Zhongshan, China.

6 Department of Cancer Prevention Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.

7 The Second Clinical Faculty of Henan University of Chinese Medicine, Zhengzhou, China.

8 Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.

9 Department of Experimental Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China. Electronic address: zengmsh@sysucc.org.cn.

Background: 

Early detection and screening of oesophageal squamous cell carcinoma rely on upper gastrointestinal endoscopy, which is not feasible for population-wide implementation. Tumour marker-based blood tests offer a potential alternative. However, the sensitivity of current clinical protein detection technologies is inadequate for identifying low-abundance circulating tumour biomarkers, leading to poor discrimination between individuals with and without cancer. We aimed to develop a highly sensitive blood test tool to improve detection of oesophageal squamous cell carcinoma.

Methods: 

We designed a detection platform named SENSORS and validated its effectiveness by comparing its performance in detecting the selected serological biomarkers MMP13 and SCC against ELISA and electrochemiluminescence immunoassay (ECLIA). We then developed a SENSORS-based oesophageal squamous cell carcinoma adjunct diagnostic system (with potential applications in screening and triage under clinical supervision) to classify individuals with oesophageal squamous cell carcinoma and healthy controls in a retrospective study including participants (cohort I) from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China), Henan Cancer Hospital (HNCH; Zhengzhou, China), and Cancer Hospital of Shantou University Medical College (CHSUMC; Shantou, China). The inclusion criteria were age 18 years or older, pathologically confirmed primary oesophageal squamous cell carcinoma, and no cancer treatments before serum sample collection. Participants without oesophageal-related diseases were recruited from the health examination department as the control group. The SENSORS-based diagnostic system is based on a multivariable logistic regression model that uses the detection values of SENSORS as the input and outputs a risk score for the predicted likelihood of oesophageal squamous cell carcinoma. We further evaluated the clinical utility of the system in an independent prospective multicentre study with different participants selected from the same three institutions. Patients with newly diagnosed oesophageal-related diseases without previous cancer treatment were enrolled. The inclusion criteria for healthy controls were no obvious abnormalities in routine blood and tumour marker tests, no oesophageal-associated diseases, and no history of cancer. Finally, we assessed whether classification could be improved by integrating machine-learning algorithms with the system, which combined baseline clinical characteristics, epidemiological risk factors, and serological tumour marker concentrations. Retrospective SYSUCC cohort I (randomly assigned [7:3] to a training set and an internal validation set) and three prospective validation sets (SYSUCC cohort II [internal validation], HNCH cohort II [external validation], and CHSUMC cohort II [external validation]) were used in this step. Six machine-learning algorithms were compared (the least absolute shrinkage and selector operator regression, ridge regression, random forest, logistic regression, support vector machine, and neural network), and the best-performing algorithm was chosen as the final prediction model. Performance of SENSORS and the SENSORS-based diagnostic system was primarily assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Results: 

Between Oct 1, 2017, and April 30, 2020, 1051 participants were included in the retrospective study. In the prospective diagnostic study, 924 participants were included from April 2, 2022, to Feb 2, 2023. Compared with ELISA (108.90 pg/mL) and ECLIA (41.79 pg/mL), SENSORS (243.03 fg/mL) showed 448 times and 172 times improvements, respectively. In the three retrospective validation sets, the SENSORS-based diagnostic system achieved AUCs of 0.95 (95% CI 0.90-0.99) in the SYSUCC internal validation set, 0.93 (0.89-0.97) in the HNCH external validation set, and 0.98 (0.97-1.00) in the CHSUMC external validation set, sensitivities of 87.1% (79.3-92.3), 98.6% (94.4-99.8), and 93.5% (88.1-96.7), and specificities of 88.9% (75.2-95.8), 74.6% (61.3-84.6), and 92.1% (81.7-97.0), respectively, successfully distinguishing between patients with oesophageal squamous cell carcinoma and healthy controls. Additionally, in three prospective validation cohorts, it yielded sensitivities of 90.9% (95% CI 86.1-94.2) for SYSUCC, 84.8% (76.1-90.8) for HNCH, and 95.2% (85.6-98.7) for CHSUMC. Of the six machine-learning algorithms compared, the random forest model showed the best performance. A feature selection step identified five features to have the highest performance to predictions (SCC, age, MMP13, CEA, and NSE) and a simplified random forest model using these five features further improved classification, achieving sensitivities of 98.2% (95% CI 93.2-99.7) in the internal validation set from retrospective SYSUCC cohort I, 94.1% (89.9-96.7) in SYSUCC prospective cohort II, 88.6% (80.5-93.7) in HNCH prospective cohort II, and 98.4% (90.2-99.9) in CHSUMC prospective cohort II. 

Conclusions: 

The SENSORS system facilitates highly sensitive detection of oesophageal squamous cell carcinoma tumour biomarkers, overcoming the limitations of detecting low-abundance circulating proteins, and could substantially improve oesophageal squamous cell carcinoma diagnostics. This method could act as a minimally invasive screening tool, potentially reducing the need for unnecessary endoscopies.

[CITATION]: Yu Wang, Shan Xing, Yi-Wei Xu, et al. Highly sensitive detection platform-based diagnosis of Oesophageal squamous cell carcinoma in China: a multicentre, case–control, diagnostic study. Lancet Digit Health. 2024 Oct;6(10): e705-e717.

[DOI]: 10.1016/S2589-7500(24)00153-5.

[IF]: 23.8

02

Yinjun Dong1 , Zhendan Wang2 , Xinying Hu3 , Yuhong Sun4 , Jingjie Qin3 , Qiming Qin1 , Shuguang Liu1 , Shuanghu Yuan3,5, Jinming Yu3 , and Yuchun Wei3 

1 Department of Esophageal Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

2 Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

3 Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

4 Department of Pathology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

5 Department of Radiation Oncology, Division of Life Sciences and Medicine, First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, China.

Background: 

This single-center, single-arm, phase II trial (ChiCTR2100050057) investigated the ability of 18F-labeled fibroblast activation protein inhibitor ([18F]AlF-NOTA-FAPI-04, denoted as 18F-FAPI) PET/CT to predict the response to neoadjuvant camrelizumab plus chemotherapy (nCC) in locally advanced esophageal squamous cell carcinoma (LA-ESCC). 

Methods: 

This study included 32 newly diagnosed LA-ESCC participants who underwent 18F-FAPI PET/CT at baseline, of whom 23 also underwent scanning after 2 cycles of nCC. The participants underwent surgery after 2 cycles of nCC. Recorded PET parameters included maximum, peak, and mean SUVs and tumor-to-background ratios (TBRs), metabolic tumor volume, and total lesion FAP expression. PET parameters were compared between patient groups with good and poor pathologic responses, and the predictive performance for treatment response was analyzed.

Results: 

The good and poor response groups each included 16 participants (16/32, 50.0%). On 18F-FAPI PET/CT, the posttreatment SUVs were significantly lower in good responders than in poor responders, whereas the changes in SUVs with treatment were significantly higher (all P < 0.05). SUVmax (area under the curve [AUC], 0.87; P = 0.0026), SUVpeak (AUC, 0.89; P = 0.0017), SUVmean (AUC, 0.88; P = 0.0021), TBRmax (AUC, 0.86; P = 0.0031), and TBRmean (AUC, 0.88; P = 0.0021) after nCC were significant predictors of pathologic response to nCC, with sensitivities of 63.64%–81.82% and specificities of 83.33%–100%. Changes in SUVmax (AUC, 0.81; P = 0.0116), SUVpeak (AUC, 0.82; P = 0.0097), SUVmean (AUC, 0.81; P = 0.0116), and TBRmean (AUC, 0.74; P = 0.0489) also were significant predictors of the pathologic response to nCC, with sensitivities and specificities in similar ranges.

Conclusion: 

18F-FAPI PET/CT parameters after treatment and their changes from baseline can predict the pathologic response to nCC in LA-ESCC participants.

[CITATION]: Dong Y, Wang Z, Hu X, et al. [18F]AlF-NOTA-FAPI-04 PET/CT for Predicting Pathologic Response of Resectable Esophageal Squamous Cell Carcinoma to Neoadjuvant Camrelizumab and Chemotherapy: A Phase II Clinical Trial. J Nucl Med. 2024 Sep 26:jnumed.124.268557. 

[DOI]: 10.2967/jnumed.124.268557.

[IF]:9.1