Fig 1.
MB and TCGA patient demographics and survival.
(A) Patient demographics of the MB subcohort. (B) Patient demographics of the TCGA subcohort. (C) Overall and (D) relapse-free, progression-free or disease specific survival of the MB and TCGA cohorts. (E) Kaplan Meier curves comparing low vs high risk patients of the MB and (F) the TCGA cohorts.
Fig 2.
The T-GAN-D robustly stratifies low and high risk breast cancer patients.
(A) Workflow of the data processing, including the schematics of the generator network and its adversary, the discriminator network. Together these result in an AC-WGAN-GP architecture. After the conversion of patient transcriptome profiles into images, 4/5 of the MB dataset was used to train the GAN’s discriminator. After 1000 epochs, the trained discriminator was used as a standalone classifier to separate the remaining 1/5 patients of the dataset into low and high risk categories. (B) Kaplan-Meier curves separating low vs. high risk patients as predicted with the T-GAN-D (iteration 1 of the 5-fold CV shown as representative). (C) Kaplan-Meier curves generated pooling the category predictions obtained for all patients of the MB dataset after five independent CV runs. (D) Separation of low vs. high risk patients predicted with a classical CNN on the same subset used in B and (E) comparison obtained pooling the predictions of five independent CV runs. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and High risk groups (dashed red line) are shown top to bottom in D and E.
Fig 3.
Introducing the independent TCGA cohort improves MB patient classification.
(A) Schematic representing the training strategy: rescaled data from the entire TCGA cohort were merged with 4/5 of the MB cohort to train the T-GAN-D, which was subsequently used to predict the risk class of the remaining 1/5 of MB patients. The process was iterated 5 times. (B) Kaplan-Meier curves based on the pooled predictions of the T-GAN-D trained on both cohorts. (C) Kaplan-Meier curves separating low vs. high risk patients predicted with the CNN that was trained after merging the MB and the TCGA cohorts. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and high risk groups (dashed red line) are shown top to bottom in B and C.
Fig 4.
The T-GAN-D outperforms classical biomarkers after merging the MB and TCGA cohorts and significantly stratifies early stage MB patients.
(A) Comparison of the hazard ratios (Cox model, univariate) of a multi-transcript signature (ROR-P) and established prognostic biomarkers (ER, HER2, PR) vs. the CNN and the T-GAN-D before and after cohort merging. (B) Multivariate Cox hazard ratio of the T-GAN-D compared to ROR-P and receptor status and (C) disease stage. (D) Kaplan -Meier curves of Stage I and (E) Stage II patients stratified by the T-GAN-D into low and high risk categories.
Fig 5.
The T-GAN-D stratifies TCGA patients despite these being scarcely represented in the merged training set.
(A) Schematic representing the training strategy: rescaled data from the entire MB cohort were merged with 4/5 of the TCGA cohort to train the T-GAN-D, which was subsequently used to predict the risk class of the remaining 1/5 of TCGA patients. The process was iterated 5 times. (B) Stratification of the TCGA patients by T-GAN-D trained on the merged dataset and (C) the MB dataset alone. Kaplan-Meier curves were generated pooling the predictions of all iterations of the 5-fold CV. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and High risk groups (dashed red line) are shown top to bottom in B and C.