Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Pan cancer subtype: Mass-spectrometry-based proteomic data from the CPTAC Confirmatory/Discovery cohorts were used to categorize 532 cases (representing five tissue-based cancer types) into ten different pan-cancer subtypes of cancer (K1 to K10). Unsupervised clustering analysis was performed using the top 2000 most variable proteins from the CPTAC Confirmatory/Discovery total protein data-set across the five CPTAC projects, using log-transformed expression values centered to standard deviations from the median within each cancer type

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

We systematically assessed pathway-level somatic alterations (by small mutation or copy number alteration) across tumors with combined proteomic, whole-exome, and CNA data, involving key pathways and genes previously annotated across multiple cancer types based on domain knowledge (pathway annotations from PMID: 33568653)

Z-values represent standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples.

Pan cancer subtype2: We identified 11 pan-cancer, proteome based subtypes (s1 to s11), using mass-spectrometry-based proteomic data from a compendium dataset of 2002 primary tumors compiled from 17 studies and 14 cancer types. Unsupervised clustering analysis was performed using the top 2000 most variable proteins from this compendium dataset, using log-transformed expression values centered to standard deviations from the median within each cancer type. The s11 subtype is specific to glioblastomas and pediatric brain tumors