Personalizing Colon Cancer Adjuvant Therapy: Selecting Optimal Treatments for Individual Patients

医学 结直肠癌 西妥昔单抗 肿瘤科 贝伐单抗 微卫星不稳定性 克拉斯 内科学 奥沙利铂 阶段(地层学) 辅助治疗 疾病 生物标志物 化疗 癌症 等位基因 古生物学 生物化学 化学 生物 微卫星 基因
作者
Rodrigo Dienstmann,Ramón Salazar,Josep Tabernero
出处
期刊:Journal of Clinical Oncology [Lippincott Williams & Wilkins]
卷期号:33 (16): 1787-1796 被引量:340
标识
DOI:10.1200/jco.2014.60.0213
摘要

For more than three decades, postoperative chemotherapy—initially fluoropyrimidines and more recently combinations with oxaliplatin—has reduced the risk of tumor recurrence and improved survival for patients with resected colon cancer. Although universally recommended for patients with stage III disease, there is no consensus about the survival benefit of postoperative chemotherapy in stage II colon cancer. The most recent adjuvant clinical trials have not shown any value for adding targeted agents, namely bevacizumab and cetuximab, to standard chemotherapies in stage III disease, despite improved outcomes in the metastatic setting. However, biomarker analyses of multiple studies strongly support the feasibility of refining risk stratification in colon cancer by factoring in molecular characteristics with pathologic tumor staging. In stage II disease, for example, microsatellite instability supports observation after surgery. Furthermore, the value of BRAF or KRAS mutations as additional risk factors in stage III disease is greater when microsatellite status and tumor location are taken into account. Validated predictive markers of adjuvant chemotherapy benefit for stage II or III colon cancer are lacking, but intensive research is ongoing. Recent advances in understanding the biologic hallmarks and drivers of early-stage disease as well as the micrometastatic environment are expected to translate into therapeutic strategies tailored to select patients. This review focuses on the pathologic, molecular, and gene expression characterizations of early-stage colon cancer; new insights into prognostication; and emerging predictive biomarkers that could ultimately help define the optimal adjuvant treatments for patients in routine clinical practice.
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