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Gene-Mutation-Based...
Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients
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- Johnson, Heather (author)
- Olympia Diagnostics, Sunnyvale, CA 94086,
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- El-Schich, Zahra (author)
- Malmö universitet,Institutionen för biomedicinsk vetenskap (BMV)
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- Amjad, Ali (author)
- Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten),Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden
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- Zhang, Xuhui (author)
- Department of Bio-Diagnosis, Institute of Basic Medical Sciences, Beijing 100005, China
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- Simoulis, Athanasios (author)
- Department of Clinical Pathology and Cytology, Skåne University Hospital, SE-205 02 Malmö, Sweden
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- Gjörloff Wingren, Anette (author)
- Malmö universitet,Institutionen för biomedicinsk vetenskap (BMV)
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- Persson, Jenny L., Professor (author)
- Malmö universitet,Umeå universitet,Institutionen för molekylärbiologi (Medicinska fakulteten),Department of Biomedical Sciences, Malmö University, Malmö, Sweden,Institutionen för biomedicinsk vetenskap (BMV),Department of Molecular Biology, Umeå University, SE-901 87 Umeå, Sweden
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(creator_code:org_t)
- 2022-04-18
- 2022
- English.
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In: Cancers. - : MDPI. - 2072-6694. ; 14:8
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Abstract
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- PURPOSE: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors.METHODS: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters.RESULTS: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001).CONCLUSION: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- KRAS
- algorithm
- colorectal cancer biomarkers
- colorectal cancer metastasis
- colorectal cancer progression
- gene mutations
- biomedical laboratory science
- biomedicinsk laboratorievetenskap
- Computer Systems
- datorteknik
- Clinical Genetics
- klinisk genetik
Publication and Content Type
- ref (subject category)
- art (subject category)
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