Wei, Y;
Papachristou, N;
Mueller, S;
Genomics England Research Consortium;
Chang, WH;
Lai, AG;
(2021)
Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project.
BMC Research Notes
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, Article 385. 10.1186/s13104-021-05789-0.
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Abstract
OBJECTIVE: The objective of this study was to employ ensemble clustering and tree-based risk model approaches to identify interactions between clinicogenomic features for colorectal cancer using the 100,000 Genomes Project. RESULTS: Among the 2211 patients with colorectal cancer (mean age of diagnosis: 67.7; 59.7% male), 16.3%, 36.3%, 39.0% and 8.4% had stage 1, 2, 3 and 4 cancers, respectively. Almost every patient had surgery (99.7%), 47.4% had chemotherapy, 7.6% had radiotherapy and 1.4% had immunotherapy. On average, tumour mutational burden (TMB) was 18 mutations/Mb and 34.4%, 31.3% and 25.7% of patients had structural or copy number mutations in KRAS, BRAF and NRAS, respectively. In the fully adjusted Cox model, patients with advanced cancer [stage 3 hazard ratio (HR) = 3.2; p < 0.001; stage 4 HR = 10.2; p < 0.001] and those who had immunotherapy (HR = 1.8; p < 0.04) or radiotherapy (HR = 1.5; p < 0.02) treatment had a higher risk of dying. The ensemble clustering approach generated four distinct clusters where patients in cluster 2 had the best survival outcomes (1-year: 98.7%; 2-year: 96.7%; 3-year: 93.0%) while patients in cluster 3 (1-year: 87.9; 2-year: 70.0%; 3-year: 53.1%) had the worst outcomes. Kaplan-Meier analysis and log rank test revealed that the clusters were separated into distinct prognostic groups (p < 0.0001). Survival tree or recursive partitioning analyses were performed to further explore risk groups within each cluster. Among patients in cluster 2, for example, interactions between cancer stage, grade, radiotherapy, TMB, BRAF mutation status were identified. Patients with stage 4 cancer and TMB ≥ 1.6 mutations/Mb had 4 times higher risk of dying relative to the baseline hazard in that cluster.
| Type: | Article |
|---|---|
| Title: | Application of ensemble clustering and survival tree analysis for identifying prognostic clinicogenomic features in patients with colorectal cancer from the 100,000 Genomes Project |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13104-021-05789-0 |
| Publisher version: | https://doi.org/10.1186/s13104-021-05789-0 |
| Language: | English |
| Additional information: | © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| Keywords: | Cluster Analysis, Colorectal Neoplasms, Female, Humans, Male, Prognosis, Radiation Oncology, Survival Analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10136014 |
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