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Fig. 6 | Genome Biology

Fig. 6

From: Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

Fig. 6

A schematic presentation of the results of the study. Cancer evolution is shaped by negative and positive selection leading to enrichment or depletion of cells with distinct aneuploidy patterns. In the gain model (left), main contributors to positive selection of gained chromosome arms are: (1) high oncogene density, (2) high expression of genes in the cancer tissue, and (3) high essential gene density. A major contributor to negative selection is high tumor suppressor gene density. Importantly, the density of TSGs is more important than the density of OGs for predicting chromosome-arm gains. In the loss model (right), a main contributor to positive selection of lost chromosome arms is high tumor suppressor gene density. Major contributors to negative selection are high oncogene density, high expression of genes in the cancer tissue, low compensation by paralogs, and high density of essential genes. In both models, the features associated with negative selection have higher overall contribution than features associated with positive selection. The thickness of the borders of the boxes reflects the relative contribution of the features to the model

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