Additional file 1 of Machine learning to improve interpretability of clinical, radiological and panel-based genomic data of glioma grade 4 patients undergoing surgical resection
Creators: name: Dal Bo, Michele
name: Polano, Maurizio
name: Ius, Tamara
name: Di Cintio, Federica
name: Mondello, Alessia
name: Manini, Ivana
name: Pegolo, Enrico
name: Cesselli, Daniela
name: Di Loreto, Carla
name: Skrap, Miran
name: Toffoli, Giuseppe
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Plant Biology
subject: Virology

description: Additional file 1: Figure S1. Distribution and classification of the gene alterations found in the 102 sequenced GG4. cases. A) Distribution of the type of alterations (e.g. missense mutations, nonsense mutations, deletions, amplifications, multihits, complex events) and frequencies for the 20 genes most frequently found altered are shown. The number of sequenced cases found altered for each gene is also shown. Figure S2. ML model obtained by considering somatic mutations involving genes belonging to the PI3K-AKT-mTOR signaling pathway (WP3844). A) Feature importance ranked by the "mean absolute magnitude" of the SHAP values of the model obtained for the 71 GG4 cases with available TMB values using a dataset (dataset 4) that included 27 features constituted by 16 genes, 10 clinical and surgical variables and the biomarker TMB. This selection was chosen in order to circumscribe the ML approach to the genes belonging to the PI3K-AKT-mTOR signaling pathway (WP3844). Upper panel: mean absolute values corresponding tothe magnitude of feature importance. Lower panel: summary plots for SHAP values; for each considered feature, a single patient is represented by one point. Along the x axis the position of a point corresponds to the logarithm of the mortality risk associated with that feature for a specific patients. This value corresponds to the impact that the feature had on the model output for that specific patient. Along the y axis, the different features are disposed according to their importance corresponding to the mean of their absolute SHAP values. Features with the higher importance are disposed on the upper part of the summary plots. Data clusters with SHAP values around zero indicate low impact on the model. SHAP, Shapley Additive exPlanation. B) Circulating barplot recapitulating the contribution in predicting OS of clinical/surgical parameters, TMB values and somatic gene mutations included in the model. Figure S3. Impact of TMB on survival in the TCGA high grade glioma series. 1 Kaplan-Meier curves comparing the OS intervals of cases with high TMB (TMB ≥ 1.7, red line) and cases with low TMB (TMB &lt;1.7, blu line). The p-value reported refers to the log-rank test. Table S1. Clinical characterization of the GG4 cohort (102 cases). Table S2. Categorization of variables in datasets. Table S3. Dataset name and composition of the datasets used with the relative reported metrics from xgboost analysis. Table S4. Detailed lists of clinical/surgical variables and gene alterations composing datasests employed for the xgboost analysis.