Biomarkers of Treatment Response in Liver Cancer
Biomarkers serve as objective measures of treatment response to guide patients towards the most appropriate therapies. Yet, in the era of big data, pinpointing promising biomarkers remains a challenging endeavor. AI is revolutionizing translational medicine by improving the efficiency and accuracy of biomarker identification. Here, we used Causaly to identify and prioritize biomarkers of sorafenib […]
AI: Revolutionizing Translational Medicine
Biomarkers serve as objective measures of treatment response to guide patients towards the most appropriate therapies and minimizing unnecessary treatments. Yet, in the era of big data, pinpointing promising biomarkers remains a challenging endeavor. AI is revolutionizing translational medicine by improving the efficiency and accuracy of biomarker identification. Here, we used Causaly’s human-centric AI to identify and prioritize sorafenib and lenvatinib treatment response biomarkers in hepatocellular carcinoma (HCC).
Treating Hepatocellular Carcinoma (HCC)
HCC, often diagnosed at advanced stages with limited curative options,¹ has a concerning 5-year survival rate of just 10% in the U.S.² The choice of treatment is therefore imperative. Treatment modalities for HCC include liver transplantation, resection and radiofrequency ablation,³ and systemic drugs including sorafenib and lenvatinib.⁴ In this example, we utilized Causaly Cloud to explore treatment response biomarkers to sorafenib and lenvatinib in HCC.
Identifying Biomarkers of HCC
Using Causaly Cloud, 9,000+ biomarkers for HCC were extracted from the entire volume of biomedical literature, as visualized in the dendrogram in Figure 1.
Biomarkers of Treatment Response
Sorafenib Treatment Response
Two biomarkers of sorafenib treatment responses with the strongest evidence were runt-related transcription factor 2 (RUNX2) and fibroblast growth factor 19 (FGF19).
Lenvatinib Treatment Response
Of the identified levatinib treatment response biomarkers, AFP and angiopoietin-2 (Ang-2) were selected for further exploration.
The exploration of biomarkers of treatment response is pivotal to advancing personalized medicine. Such biomarkers offer promising avenues for predicting therapeutic efficacy and tailoring treatments to enhance outcomes for patients.
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