Extended Data Fig. 5: Performance of MOFA factors to predict survival.
a) Increase in area under the curve (AUC) as a function of percentage of change compared to histological classification. b) Density of survival time within the MESOMICS cohort. c) Integral AUC (iAUC) of twenty-two Cox proportional hazards survival models based on: (i) the three histopathological types (MME, MMB, and MMS); (ii) the proportion of sarcomatoid content; (iii) the log2 ratio of CLDN15/VIM (C/V) expression proposed by Bueno and colleagues; (iv), (v) and (vi) the E score, S score, and combining both scores from Blum and colleagues, respectively; (vii) an Artificial Intelligence (AI) prognostic score; (viii-xi) the one-dimensional summary of molecular data using LFs as a continuous variable; (xii-xvii), the two-dimensional summary of molecular data using either each combination of 2 LFs as continuous variables, respectively; (xviii-xxi), the three-dimensional summary of molecular data using each combination of 3 LFs as continuous variables; and (xxii), the four-dimensional summary of molecular data using all 4 LFs. Bars represent the mean values and error bars their standard error. Panels (a-c) present the out-of-sample accuracy within the MESOMICS cohort (4-fold cross-validation on n = 120 individuals), while (d-f) present the out-of-sample accuracy within the TCGA cohort (2000 bootstraps on n = 73 individuals). The model fit accuracy (no split between training and test sets) on MESOMICS and TCGA cohort are presented in Supplementary Table 17.
