Zihang Mai

Background: After radical surgery, patients with esophageal cancer should undergo long-term surveillance of disease relapse. However, the optimal follow-up strategy remains to be explored. Method: A total of 4688 patients were recruited. Recursive partition analysis was applied to develop recurrence risk stratification for patients. The follow-up strategies of each stratification were developed based on monthly recurrence probability and validated by bootstrap validation and an external dataset. A Markov decision-analytic model was constructed to evaluate the cost-effectiveness of the follow-up strategies. Results: Patients were stratified into four groups according to four pathological features. The authors applied a random survival forest to calculate the monthly recurrence probability of each group. Based on the temporal distribution of recurrences, the authors further established surveillance strategies for four groups. The strategies were validated as optimal protocols by bootstrap resampling and another dataset. Markov cost-effective analysis indicated that our recommended strategies outperformed the mainstream protocols from guidelines. Using less than 12 visits across the first 5 years on average, our follow-up strategies were more efficient than the NCCN recommended strategies (14 visits average). Our results also supported the computerized tomography from the neck to the upper abdomen as a routine examination and PETCT of distant metastasis for some groups with high risks. Conclusion: Our study provided data-driven evidence of personalized and economic follow-up strategies for esophageal cancer patients and shed light on follow-up optimization for other cancer types.

Background: The overall prognosis of primary mediastinal germ cell tumors (PMGCTs) is poor and the associated prognostic factors are not fully understood. Our goal was to investigate the prognostic factors of PMGCTs and to develop a validated prognostic prediction model. Materials and Methods: A total of 114 PMGCTs with specific pathological types were included in this study. Clinicopathological characteristics of nonseminomatous PMGCTs and mediastinal seminomas were compared using the χ ² or Fisher’s exact test. Independent prognostic factors of nonseminomatous PMGCTs screened using the univariate and multivariate Cox regression analysis were then used to generate a nomogram. The predictive performance of the nomogram was evaluated using the concordance index, decision curve, and the area under the receiver operating characteristic curve (AUC) and validated by bootstrap resampling. The Kaplan–Meier curves of independent prognostic factors were analyzed. Results: This study included 71 cases of nonseminomatous PMGCTs and 43 cases of mediastinal seminomas. The 3-year overall survival rates for nonseminomatous PMGCTs and mediastinal seminomas patients were 54.5 and 97.4%, respectively. The overall survival prognostic nomogram for nonseminomatous PMGCTs was established by integrating independent prognostic factors, including the Moran-Suster stage, white blood cell, hemoglobin, and platelet-lymphocyte ratio. The nomogram demonstrated good performance with a concordance index of 0.760 and the 1-year and 3-year AUC values of 0.821 and 0.833, respectively. These values were better than those of the Moran-Suster stage system. The bootstrap validation had an AUC of 0.820 (0.724–0.915) and showed a well-fitting calibration curve. Besides, patients with mediastinal seminomas showed favorable clinical outcomes and all the nine patients received neoadjuvant therapy and postoperative surgery achieved pathological complete response. Conclusion: A nomogram based on staging and blood routine examination results was established to accurately and consistently predict the prognosis of patients with nonseminomatous PMGCTs.

Identification of molecular subtypes that reflect different prognoses and treatment responses, especially immune checkpoint inhibitors (ICIs) in esophageal squamous cell carcinoma (ESCC), is essential for treatment decisions. We performed targeted sequencing in 201 patients with ESCC to discover genetic subtypes and validate our findings via multiple data sets. We identified 3 driver genes ( FCGBP , GRIN2B , and FRY ), and recurrent truncating mutations in FRY impaired its tumor-suppressive function and promoted tumor proliferation. A 3-gene mutation signature ( FAT1 , FAT3 , and FRY ) recognized a molecular subtype named “FAT/FRY” with frequent Hippo pathway–related mutations. In multiple ESCC cohorts, the patients with the FAT/FRY subtype had poorer prognosis than did patients in the WT group. Transcriptome analysis indicated that the FAT/FRY subtype was characterized by inactivation of the Hippo pathway, hypoxia, chemoresistance, higher infiltration of CD8 ⁺ T cells and activated DCs, and a transcriptome similar to that of cancer responders. Furthermore, the 3-gene signature predicted better survival for patients treated with ICIs, partially explained by its positive correlation with the tumor mutation burden and neoantigen burden. The 3-gene signature is a biomarker to recognize the FAT/FRY molecular subtype, evaluate prognosis, and select potential beneficiaries of ICIs in ESCC.

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest malignancies in China. The prognostic value of mutations, especially those in minor tumor clones, has not been systematically investigated. We conducted targeted deep sequencing to analyze the mutation status and the cancer cell fraction (CCF) of mutations in 201 ESCC patients. Our analysis showed that the prognostic effect of mutations was relevant to the CCF, and it should be considered in prognosis prediction. EP300 was a promising biomarker for overall survival, impairing prognosis in a CCF dose-dependent manner. We constructed a CCF-based predictor using a smooth clipped absolute deviation Cox model in the training set of 143 patients. The 3-year disease-free survival rates were 6.3% (95% CI: 1.6–23.9%), 29.8% (20.9–42.6%) and 70.5% (56.6–87.7%) in high-, intermediate- and low-risk patients, respectively, in the training set. The prognostic accuracy was verified in a validation set of 58 patients and the TCGA-ESCC cohort. The eight-gene model predicted prognosis independent of clinicopathological factors and the combination of our model and pathological staging markedly improved the prognostic accuracy of pathological staging alone. Our study describes a novel recurrence predictor for ESCC patients and provides a new perspective for the clinical translation of genomic findings.