CNAs Gene-associated CNAs from the TCGA data identified with the GISTIC2 tool75 were downloaded via firehose from httpsgdacbroadinstituteorg Both amplified and deleted genes were collected ultramutate

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We also obtained information on the chromosomal location of each gene from the UCSC Genome Browser (https://genome.ucsc.edu/). We used this information to group genes that were located on the same chromosome and to calculate the total number of CNAs affecting each chromosome in each cancer type.

To compare the CNAs across different cancer types, we normalized the copy number rate of each gene by the total number of CNAs affecting the corresponding chromosome in the same cancer type. This allowed us to account for the fact that some chromosomes are more commonly affected by CNAs than others.

Finally, we used principal component analysis (PCA) to visualize the similarities and differences between the CNAs across different cancer types. PCA reduces the dimensionality of the data by identifying the directions (or principal components) that capture the most variance in the data. We used the first two principal components to generate a scatter plot of the cancer types, where each point represents a cancer type and the distance between points reflects the similarity of the CNAs between them