Emergence of Predictive Metabolomic Biomarkers of Response to Neoadjuvant Chemoimmunotherapy in Triple-Negative Breast Cancer.
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Predictive performance of targeted metabolites and histological response
研究概览
简要总结
The goal of this study is to investigate whether specific metabolic profiles can predict response to neoadjuvant chemo-immunotherapy in patients with stage II and III triple-negative breast cancer (TNBC).
The main objectives are to determine:
- whether targeted metabolic pathways, specifically tryptophan and diacetylspermine metabolism, are associated with histological response to neoadjuvant treatment;
- whether untargeted metabolomics combined with machine learning approaches can identify novel biomarkers predictive of treatment response.
Participants will:
- undergo image-guided tumor sampling during the standard-of-care placement of the tumor localization clip, prior to initiation of neoadjuvant chemo-immunotherapy, in accordance with routine clinical practice at the study center;
- provide a blood sample before initiation of neoadjuvant chemo-immunotherapy;
- provide a second blood sample during the course of standard treatment; continue to receive routine clinical care, including imaging assessments and surgical management, according to standard clinical practice.
The study does not modify the patient's standard therapeutic management.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Age 18 years or older.
- •Female patient with stage II and III triple-negative breast cancer, requiring neoadjuvant chemo-immunotherapy according to the KEYNOTE-522 regimen or any neoadjuvant treatment including chemotherapy and immunotherapy validated in standard care or a clinical trial, regardless of the treatment regimen (ER < 10%, PR < 10%, HER2 0, and HER2 low).
- •Patient requiring the placement of a localization clip as part of their standard clinical care management.
- •Patient who has read the information sheet and signed the informed consent.
- •Patient affiliated with a Social Security health insurance scheme.
排除标准
- •Metastatic breast cancer documented by staging workup (PET scan or thoraco-abdomino-pelvic CT scan + bone scan).
- •Patient who has undergone primary surgery.
- •INR < 1.5; Platelets < 50,000/$\mu$L.
- •Patient presenting with multiple primary malignant tumors.
- •Patient with HIV infection, Hepatitis C, or active Hepatitis B.
- •Patient taking:
- •Dual antiplatelet therapy: Acetylsalicylic acid + Clopidogrel without the possibility of suspending Clopidogrel for 5 days,
- •Ticagrelor without the possibility of suspension for 5 days,
- •Prasugrel without the possibility of suspension for 7 days,
- •Vitamin K antagonists (acenocoumarol, warfarin, or fluindione) with INR < 2 or > 3, without the possibility of suspension.
研究组 & 干预措施
Neoadjuvant chemo-immunotherapy
Patients receiving neoadjuvant chemo-immunotherapy according to the study protocol.
干预措施: Blood sampling (Biological)
Neoadjuvant chemo-immunotherapy
Patients receiving neoadjuvant chemo-immunotherapy according to the study protocol.
干预措施: Tumor sampling (Procedure)
结局指标
主要结局
Predictive performance of targeted metabolites and histological response
时间窗: From enrollment to the end of treatment and follow-up - 8 months.
The predictive performance of targeted metabolites (tryptophan pathway and diacetylspermine) to predict post-surgical histological response will be evaluated using the area under the ROC curve (AUC), with an AUC target \> 0.8. Histological response will be assessed according to the Residual Cancer Burden (RCB) classification, ranging from RCB0 (pathological complete response) to RCB3 (presence of extensive residual disease), based on the results obtained from the surgical specimen of breast surgery.
Consistency of metabolites between plasma and tumor biopsies
时间窗: From enrollment to the end of treatment and follow-up - 8 months.
The consistency of variations in metabolites of interest between plasma samples and tumor biopsies will be evaluated by a semi-quantitative correlation between the two compartments.
次要结局
- Performance of a metabolomic signature using machine learning(From enrollment to the end of treatment and follow-up - 8 months.)
- Variations of tryptophan pathway metabolites(From enrollment to the end of treatment and follow-up - 8 months.)
- Prediction of radiological response by targeted metabolites(From enrollment to the end of treatment and follow-up - 8 months.)