Esophageal Squamous Cell Carcinoma
Conditions
Keywords
deep learning, Esophageal Squamous Cell Carcinoma, immune infiltration
Brief summary
The burden of esophageal squamous cell carcinoma (ESCC) in China is substantial, with 85% of the cancers being in the progressive stage. The treatment for advanced ESCC are extremely limited, and immunotherapy, represented by PD-1 inhibitors, has demonstrated a promising application potential. However, the effectiveness of PD-1 inhibitors varies significantly among patients with different types of ESCC, and currently, there is no effective method to predict the response to PD-1 inhibitors. In this study, investigators aim to construct a multimodal deep learning-based model to predict the level of immune infiltration and the efficacy of immunotherapy for ESCC, integrating both pathological image features and clinical information of patients with ESCC, thereby enhancing the level of individualized and precise treatment for ESCC.
Interventions
DIAGNOSTIC_TESTDNA Sequencing, RNA Sequencing
High-coverage Whole-Exome Sequencing sequencing of DNA samples from ESCC was performed. RNA expression was analyzed using the NanoString PanCancer Immuno-Oncology 360TM Panel that includes a set of more than 700 genes involved in the main biological pathways of human immunity. These experiments were performed by the Genomics platform of Institut Curie. Total RNAs were used as templates.
Sponsors
Wangluowei
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Availability of Hematoxylin and Eosin (H&E) stained images, molecular data obtained through RNA sequencing (RNA-Seq, DNA-seq), and comprehensive clinical information including patient age, gender, history of alcohol consumption, history of smoking, AJCC Tumor, Node, Metastasis Stage, specific location of oesophageal cancer occurrence, and history of reflux. 2. The sample collection is restricted to cancerous tissue, encompassing both primary tumor samples and those from metastatic sites.
Exclusion criteria
1. Patients diagnosed with adenosquamous carcinoma or presenting with a combination of other types of oesophageal cancers; 2. Cases involving combined adenocarcinoma affecting the gastroesophageal junction; 3. Individuals with high-grade tumors that have not penetrated the basement membrane, as confirmed by postoperative pathological examination; 4. Subjects in whom postoperative pathology confirms an absence of residual malignant tissue.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Immunogene signatures with predictive value for immunotherapy of ESCC | After undergoes surgery. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Prognosis and immunotherapy tolerance in ESCC patients | Follow-up for at least 1 year after undergoing surgery | The prognosis was determined through follow-up, while tolerance to immunotherapy was anticipated utilizing gene sequencing methodologies. |
Countries
China
Outcome results
None listed