Precision Medicine for the Treatment of Colorectal Cancer: the Evolution and Status of Molecular Profiling and Biomarkers

Patient-specific biomarkers form the foundation of precision medicine strategies. To realize the promise of precision medicine in patients with colorectal cancer (CRC), access to cost-effective, convenient, and safe assays is critical. Improvements in diagnostic technology have enabled ultrasensitive and specific assays to identify cell-free DNA (cfDNA) from a routine blood draw. Clinicians are already employing these minimally invasive assays to identify drivers of therapeutic resistance and measure genomic heterogeneity, particularly when tumor tissue is difficult to access or serial sampling is necessary. As cfDNA diagnostic technology continues to improve, more innovative applications are anticipated. In this review, we focus on four clinical applications for cfDNA analysis in the management of CRC: detecting minimal residual disease, monitoring treatment response in the metastatic setting, identifying drivers of treatment sensitivity and resistance, and guiding therapeutic strategies to overcome resistance.

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Organoids as a Robust Preclinical Model for Precision Medicine in Colorectal Cancer: A Systematic Review

Annals of Surgical Oncology ◽

10.1245/s10434-021-10829-x ◽

2021 ◽

Author(s):

Michael Flood ◽

Vignesh Narasimhan ◽

Kasmira Wilson ◽

Wei Mou Lim ◽

Robert Ramsay ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Precision Medicine ◽

Preclinical Model

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Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer

Journal of Molecular Cell Biology ◽

10.1093/jmcb/mjaa041 ◽

2020 ◽

Author(s):

Quanxue Li ◽

Wentao Dai ◽

Jixiang Liu ◽

Yi-Xue Li ◽

Yuan-Yuan Li

Keyword(s):

Colorectal Cancer ◽

Transcriptional Regulation ◽

Precision Medicine ◽

Predictive Accuracy ◽

Explanatory Power ◽

Therapeutic Benefit ◽

Machine Learning Algorithms ◽

Analysis Framework ◽

Gene Dysregulation ◽

Therapeutic Benefits

Abstract The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from TCGA. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation signature (4-DysReg), which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

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