Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI)...

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Bibliographic Details
Published in:International journal of molecular sciences 2024-03, Vol.25 (6), p.3220
Main Authors: Hummel, Justin J, Liu, Danlu, Tallon, Erin, Snyder, John, Warren, Wesley, Shyu, Chi-Ren, Mitchem, Jonathan, Cortese, Rene
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Cancer
Cancer therapies
Chemotherapy
Colorectal cancer
Colorectal Neoplasms - pathology
Data mining
Diagnosis
Gene Expression Regulation, Neoplastic
Genes
Genetic aspects
Genomics
Humans
Mutation
Neoplasm Recurrence, Local - pathology
Patients
Survival analysis
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Summary:Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set ( = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset ( = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25063220