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Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).
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2022 (English)In: Cancers, ISSN 2072-6694, Vol. 14, no 8, article id 2045Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 14, no 8, article id 2045
Keywords [en]
KRAS, algorithm, colorectal cancer biomarkers, colorectal cancer metastasis, colorectal cancer progression, gene mutations
National Category
Cancer and Oncology
Research subject
biomedical laboratory science; Computer Systems; Clinical Genetics
Identifiers
URN: urn:nbn:se:umu:diva-194155DOI: 10.3390/cancers14082045ISI: 000785547200001PubMedID: 35454952Scopus ID: 2-s2.0-85128405452OAI: oai:DiVA.org:umu-194155DiVA, id: diva2:1654208
Funder
EU, Horizon 2020, 721297The Kempe FoundationsSwedish Cancer Society, CAN-2017-381Swedish Research Council, 2019-01318The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), IG2013- 5595Cancerforskningsfonden i NorrlandAvailable from: 2022-04-26 Created: 2022-04-26 Last updated: 2022-12-15Bibliographically approved

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Amjad, AliPersson, Jenny L.

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CiteExportLink to record
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