Personalizing adjuvant therapy for patients with colorectal cancer.
Li YangJinlin YangAndreas KleppeHåvard E DanielsenDavid J KerrPublished in: Nature reviews. Clinical oncology (2023)
The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.
Keyphrases
- end stage renal disease
- artificial intelligence
- chronic kidney disease
- prognostic factors
- ejection fraction
- newly diagnosed
- machine learning
- early stage
- peritoneal dialysis
- deep learning
- randomized controlled trial
- oxidative stress
- squamous cell carcinoma
- dna methylation
- stem cells
- minimally invasive
- palliative care
- clinical trial
- big data
- patient reported outcomes
- mesenchymal stem cells
- optical coherence tomography
- ionic liquid
- locally advanced
- pet ct
- circulating tumor cells
- health insurance
- ultrasound guided
- double blind
- affordable care act