PMID- 35982500 OWN - NLM STAT- MEDLINE DCOM- 20220822 LR - 20220907 IS - 1479-5876 (Electronic) IS - 1479-5876 (Linking) VI - 20 IP - 1 DP - 2022 Aug 18 TI - Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients. PG - 373 LID - 10.1186/s12967-022-03572-8 [doi] LID - 373 AB - BACKGROUND: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams? METHODS: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis. RESULTS: We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). CONCLUSIONS: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis. CI - (c) 2022. The Author(s). FAU - Orostica, Karen Y AU - Orostica KY AD - Instituto de Investigacion Interdisciplinaria, Vicerrectoria Academica, Universidad de Talca, 3460000, Talca, Chile. FAU - Saez-Hidalgo, Juan AU - Saez-Hidalgo J AD - Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, 8370456, Santiago, Chile. AD - Department of Computer Science, University of Chile, 8370459, Santiago, Chile. FAU - de Santiago, Pamela R AU - de Santiago PR AD - Department of Cell and Molecular Biology, Faculty of Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile. FAU - Rivas, Solange AU - Rivas S AD - Department of Basic Clinical Oncology, Faculty of Medicine, University of Chile, Santiago, Chile. AD - Centro de Genetica Y Genomica, Instituto de Ciencias E Innovacion en Medicina, Facultad de Medicina Clinica Alemana, Universidad del Desarrollo, 7590943, Santiago, Chile. FAU - Contreras, Sebastian AU - Contreras S AD - Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, 8370456, Santiago, Chile. AD - Max Planck Institute for Dynamics and Self-Organization, Gottingen, Germany. FAU - Navarro, Gonzalo AU - Navarro G AD - Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, 8370456, Santiago, Chile. AD - Department of Computer Science, University of Chile, 8370459, Santiago, Chile. FAU - Asenjo, Juan A AU - Asenjo JA AD - Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, 8370456, Santiago, Chile. FAU - Olivera-Nappa, Alvaro AU - Olivera-Nappa A AD - Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, 8370456, Santiago, Chile. aolivera@ing.uchile.cl. FAU - Armisen, Ricardo AU - Armisen R AUID- ORCID: 0000-0003-2567-0521 AD - Centro de Genetica Y Genomica, Instituto de Ciencias E Innovacion en Medicina, Facultad de Medicina Clinica Alemana, Universidad del Desarrollo, 7590943, Santiago, Chile. rarmisen@udd.cl. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20220818 PL - England TA - J Transl Med JT - Journal of translational medicine JID - 101190741 SB - IM MH - *Adenocarcinoma of Lung/genetics/pathology MH - *Carcinoma, Non-Small-Cell Lung/pathology MH - *Carcinoma, Squamous Cell/genetics MH - Humans MH - *Lung Neoplasms/genetics/pathology MH - Mutation/genetics PMC - PMC9389677 OTO - NOTNLM OT - Clinical variables OT - Lung Adenocarcinoma (LUAD) OT - Lung Squamous Cell Carcinoma (LSCC) and Metastasis OT - Random Forest OT - Smoking COIS- R.A. declares honoraria for conferences, advisory boards, and educational activities from Roche and grants and support for scientific research from Pfizer, Roche & Thermo Fisher Scientific. EDAT- 2022/08/19 06:00 MHDA- 2022/08/23 06:00 PMCR- 2022/08/18 CRDT- 2022/08/18 23:43 PHST- 2021/12/29 00:00 [received] PHST- 2022/08/04 00:00 [accepted] PHST- 2022/08/18 23:43 [entrez] PHST- 2022/08/19 06:00 [pubmed] PHST- 2022/08/23 06:00 [medline] PHST- 2022/08/18 00:00 [pmc-release] AID - 10.1186/s12967-022-03572-8 [pii] AID - 3572 [pii] AID - 10.1186/s12967-022-03572-8 [doi] PST - epublish SO - J Transl Med. 2022 Aug 18;20(1):373. doi: 10.1186/s12967-022-03572-8.