Precision Medicine for Combined Hepatocellular-Cholangiocarcinoma
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Diagnosos
研究概览
简要总结
Our project is a large-scale characterisation of cHCC-CCA will allow us to determine which subsets harbor actionable gene alterations. We will also aim to improve diagnosis of this tumor type by the use of immunohistochemical biomarkers and the development of deep-learning based models able to help cHCC-CCA diagnosis. This will represent an important step towards precision medicine for the patients with this highly aggressive malignancy.
详细描述
- Scientific background Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare liver cancer characterized by a dual hepatocytic and biliary differentiation. It is resistant to conventional anti-cancer treatments and there is currently no effective systemic therapy available. Inter-observer agreement for diagnosis of cHCC-CCA is low, even among expert pathologists, and the development of clinical trials remain thus challenging. The molecular mechanisms that drive its progression also remain under-investigated.
- Project objectives and brief description of the methods which will be used to achieve them We aim to perform an integrative molecular, immune and phenotypical study of cHCC-CCA that will allow the distinction of different tumor subgroups linked to particular actionable genetic/immune alterations. The development of immunohistochemical markers and artificial intelligence-based approaches is also likely to improve the diagnosis of cHCC-CCA.
A overall multicentric series of 357 cHCC-CCA samples, already available in our biobanks, will be investigated by means of gene and RNA sequencing, digital pathology and immunohistochemistry in order to build a morphomolecular classification of this tumor. Spatial transcriptomics and in situ proteomics will be performed to decipher the intra-tumor heterogeneity and identify biomarkers of the different subclasses. Finally, deep-learning based models will be developed in order to 1) improve the diagnosis of cHCC-CCA and 2) identify the morphological features linked to prognosis.
• Expected results This large-scale characterisation of cHCC-CCA will allow us to determine which subsets harbor actionable gene alterations. We will also aim to improve diagnosis of this tumor type by the use of immunohistochemical biomarkers and the development of deep-learning based models able to help cHCC-CCA diagnosis. This will represent an important step towards precision medicine for the patients with this highly aggressive malignancy.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •histological diagnosis of combined tumor biological sample available
排除标准
- •unequivocal histological features
结局指标
主要结局
Diagnosos
时间窗: 3 yrs
Tumor subgroups
次要结局
未报告次要终点
研究者
Julien Calderaro
Full Professor
Inserm U955
