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Figure 6 | BMC Cancer

Figure 6

From: Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Figure 6

Improved prognostic models through non-linear interactions of pathway modules. A) For pathways that correlated with DMFS in ER+ and ER- breast cancer (Table 2), we consider corresponding Boolean interaction Cox regression models describing the pairwise interaction of any two pathways (the best model out of a total of 4, i.e up-up, up-down, down-up, down-down, is shown). y-axis labels the pathway interaction pair and best boolean model, x-axis denotes the log-likelihood of the corresponding model. (Black = log-likelihood of model for first pathway in pair, Grey = log-likelihood of model for second pathway in pair, Red = log-likelihood of the best Boolean interaction model, pink dashed line highlights those Boolean models with improved log-likelihoods). B) Heatmaps of likelihood ratio test (LRT) p-values comparing nested prognostic models. Specifically, LRT p-value for pathway p y on y-axis and pathway p x on x-axis is obtained by comparing Cox-regression models with the single pathway p x plus non-linear Boolean interaction B(p x , p y ) as predictors against the model with only p x as predictor. C) As B), but LRT p-value for pathway p y on y-axis and pathway p x on x-axis is obtained by comparing Cox-regression models with the single pathway p x plus non-linear Boolean interaction B(p x , p y ) as predictors against the model with only B(p x , p y ) as predictor. Color codes: red (P < 0.01), pink (P < 0.05), white (P > 0.05). All Cox regression were stratified regression using the cohorts as strata to account for variations in the hazard rate between cohorts.

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