Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients

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dc.contributor.authorOróstica, Karen
dc.contributor.authorSaez, Juan
dc.contributor.authorDe Santiago, Pamela
dc.contributor.authorRivas, Solange
dc.contributor.authorContreras, Sebastián
dc.contributor.authorNavarro, Gonzalo
dc.contributor.authorAsenjo, Juan
dc.contributor.authorOlivera, Álvaro
dc.contributor.authorArmisén, Ricardo
dc.date.accessioned2022-11-15T18:37:16Z
dc.date.available2022-11-15T18:37:16Z
dc.date.issued2022
dc.description.abstractAbstract Background: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohortsof cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), sum‑marises the mutational and clinical profles of diferent subtypes of Lung Cancer (LC). Mutational and clinical signa‑ tures have been used independently for tumour typifcation and prediction of metastasis in LC patients. Is it then possible to achieve better typifcations 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 diferent classes of patients. Using the TML and diferent sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classifcation models that calculate the likelihood of developing metastasis. Results: We found that LC patients diferent in age, smoking status, and tumour type had signifcantly diferent mean TMLs. Although TML was an informative feature, its efect was secondary to the "tumour stage" feature. However, its contribution to the classifcation 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 perfor‑mance, 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 evi‑ dence of the need for comprehensive diagnostic tools for metastasis.es
dc.description.versionVersión publicadaes
dc.identifier.citationOróstica KY, Saez-Hidalgo J, de Santiago PR, Rivas S, Contreras S, Navarro G, Asenjo JA, Olivera-Nappa Á, Armisén R. Total mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patients. J Transl Med. 2022 Aug 18;20(1):373. doi:10.1186/s12967-022-03572-8es
dc.identifier.urihttps://doi.org/doi:10.1186/s12967-022-03572-8es
dc.identifier.urihttp://hdl.handle.net/11447/6673
dc.language.isoenes
dc.subjectRandom Forestes
dc.subjectSmokinges
dc.subjectClinical variableses
dc.subjectLung Adenocarcinoma (LUAD)es
dc.subjectLung Squamous Cell Carcinoma (LSCC) and Metastasises
dc.titleTotal mutational load and clinical features as predictors of the metastatic status in lung adenocarcinoma and squamous cell carcinoma patientses
dc.typeArticlees
dcterms.sourceJournal of translational medicinees

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