Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA ‘microsatellite instability/CpG island methylation phenotype’ transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
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This work was supported by National Cancer Institute (NCI) CPTAC awards U24CA159988, U24CA160035, and U24CA160034; by NCI SPORE award P50CA095103 and NCI Cancer Center Support Grant P30CA068485; by National Institutes of Health grant GM088822; and by contract 13XS029 from Leidos Biomedical Research, Inc. Genomics data for this study were generated by The Cancer Genome Atlas pilot project established by the NCI and the National Human Genome Research Institute. Information about TCGA and the investigators and institutions comprising the TCGA research network can be found at http://cancergenome.nih.gov/.
The authors declare no competing financial interests.
All of the primary mass spectrometry data on TCGA tumour samples are deposited at the CPTAC Data Coordinating Center as raw and mzML files for public access (https://cptac-data-portal.georgetown.edu).
Extended data figures and tables
Protein was extracted from frozen tumour tissue and used to generate tryptic digests. The resulting tryptic peptides were fractionated using off-line basic reverse-phase (high-pressure) liquid chromatography (basic RPLC). Collected fractions were pooled and used for reverse-phase HPLC in line with a Thermo Orbitrap-Velos MS instrument. Raw data were processed by MSConvert and then used for database and spectral library searching using three different search engines (Myrimatch, Pepitome and MS-GF+). Identified peptides were assembled using IDPicker 3 with selected filters as described in the methods. IDPicker 3 stores its protein assemblies for a specified set of filters in the idpDB format. These SQLite databases associate spectra with peptides, peptides with proteins, and LC-MS/MS experiments with a hierarchy of experiments.
Extended Data Figure 2 Relaxing the false discovery rate of peptide-spectrum match for high-confident proteins increases spectral counts.
To increase spectral counts and improve statistical comparisons, we first created a protein assembly that maximized the number of proteins identified (at 0.1% peptide-spectrum match false discovery rate (PSM FDR)) and then relaxed the PSM FDR to 1% exclusively for the set of confidently identified proteins. This strategy led to increased spectral counts from 4,896,831 to 6,299,756, a 29% increase. a, Spectral count plot of all 7,526 confidently identified proteins demonstrates the increase in the absolute number of spectra identified for each protein, but no decrease for any of the proteins. Each dot in the figure represents one of the 7,526 proteins; x axis and y axis represent the spectral counts obtained in the data sets with 0.1% and 1% PSM FDR, respectively, both plotted on a log scale. b, Density plot showing the distribution of PSM FDR scores for all rescued PSMs. Rescued PSMs are of high quality with a median PSM FDR score of less than 0.2%, indicating the maintained integrity of the data set.
a, Summary of total RNA-Seq read counts and mapping results for individual samples. b, Distribution of percentage sequence coverage in exons for individual samples. Among all 228,157 exons, 76% were expressed, but only 64% had an average coverage greater than 1. Exons with no coverage were not included in the box plots. c, Number of missense somatic variants detected by RNA-seq in individual samples. Approximately 54% of the mutation positions were covered by RNA-seq reads and only 43% were covered by three or more reads.
Single amino acid variants (SAAVs) identified in the TCGA shotgun data set were validated using parallel-reaction-monitoring (PRM) analyses. Three distinct SAAVs in four TCGA samples were selected for validation. The TCGA samples were freshly prepared in the same manner as the original samples analysed by shotgun proteomics. Each sample was spiked with 12.5 fmol μl−1 of a mixture of all isotopically labelled peptides. Using an inclusion list containing the precursor m/z values representing both unlabelled (endogenous) and labelled peptides, each fraction was analysed by PRM for the variant peptides. This figure shows the PRM data for the variant sequence LVVVGADGVGK (KRAS(Gly12Asp) in TCGA-AA-3818. a, The MS/MS spectrum identified in the initial shotgun analyses. b, The annotated MS/MS spectrum of the unlabelled endogenous variant peptide in the PRM analysis. c, The annotated MS/MS spectrum of the spiked, labelled peptide in the PRM analysis. d, The chromatographic trace showing the overlapping transitions and retention time of the endogenous variant peptide. e, The chromatographic trace showing the overlapping transitions and retention time of the labelled variant peptide.
Extended Data Figure 5 Platform evaluation and analysis method selection using quality control samples.
a, The lower-left half (uncoloured) depicts pairwise scatter plots of the samples, with x and y axes representing quantile-normalized spectral counts for samples in corresponding columns and rows, respectively. The upper-right half (red) depicts pairwise Spearman’s correlation coefficients for the same comparisons. b, For each normalization method (none, global, quantile and NSAF), we calculated the intraclass correlation coefficients (ICCs) for individual proteins in the quality control data set. The analysis was done for the top 1,000, 500 or 100 proteins with the largest variance and the cumulative fraction curves were plotted. In most scenarios, quantile normalization generated slightly higher ICC scores than global normalization, and both methods clearly outperformed the NSAF normalization. c, We sorted all proteins in the quality control data set based on their total spectral counts and then divided the proteins into 10 bins with equal number of proteins. Average spectral count ranges for each bin are shown in the brackets in the legend box. For each bin, we calculated the ICCs for individual proteins in the bin. The analysis was done for the top 300, 200 or 100 proteins with the largest variance in each bin. The cumulative fraction curves were plotted. Protein bins with spectral counts less than 1.4 showed clearly lower ICC scores, whereas the ICC score curves started to converge when the average spectral count was greater than 1.4.
a, Evaluation of the length bias in different RNA-Seq-based gene abundance estimation methods. The plot shows the distribution of correlation between gene length and estimated transcript abundance based on FPKM (fragments per kilobase of exon per million fragments mapped, blue curve) and RSEM (RNA-seq expectation maximization, red curve), respectively. FPKM measure is independent of gene length, whereas the RSEM measure strongly correlates with gene length. b, Relationship between mRNA–protein correlation and the stability of the molecules. Human genes were separated into four categories based on the mRNA and protein half-lives of their mouse orthologues: stable mRNA–stable protein; stable mRNA–unstable protein, unstable mRNA–stable protein, and unstable mRNA–unstable protein. Distribution of mRNA–protein correlations for genes in each category was plotted in the box plots. Genes with stable mRNA and stable protein showed relatively higher mRNA–protein correlation whereas those with unstable mRNA and unstable protein showed relatively lower mRNA–protein correlation. Only common genes in both our study and the mouse study were included in the analysis. The total number of genes in each category (N) is labelled in the figure. The P value indicating correlation difference among the four categories was calculated based on the Kruskal–Wallis non-parametric analysis of variance (ANOVA) test. The P value indicating correlation difference between the stable mRNA–stable protein group and the unstable mRNA–unstable protein group was calculated based on the two-sided Wilcoxon rank-sum test.
Extended Data Figure 7 mRNA and protein-level cis-effect of copy number alterations in focal amplification, focal deletion and non-focal regions.
The figure plots cumulative fraction curves of copy number alteration (CNA)–mRNA (dashed lines) and CNA–protein (solid lines) expression correlations for genes in the focal amplification regions (red), focal deletion regions (green), and non-focal regions (blue), respectively. Focal alteration regions were defined in the TCGA study. Any chromosomal regions outside the focal amplification and deletion regions were considered as non-focal regions. CNA–mRNA correlations were significantly higher than CNA–protein correlations for genes in any of the three groups. Moreover, genes in the focal amplification regions showed the highest level of CNA–mRNA and CNA–protein correlations among the three groups of genes. P values were based on the two-sided Kolmogorov–Smirnov test.
Extended Data Figure 8 HNF4α isoforms and the effect of HNF4A shRNA on the proliferation of colon cancer cells.
a, Multiple sequence alignment of the HNF4α isoforms, with peptides detected by shotgun proteomics and sequences corresponding to the shRNA target sequences highlighted. Different colours of the letters indicate different levels of sequence coverage in the shotgun proteomics study, as indicated by the colour scale bar. Yellow boxes highlight sequences corresponding to the shRNA target sequences. TRCN0000019193 specifically targets P1 promoter-driven isoforms, whereas the other two target both types of isoforms. b–d, The P1-HNF4α-specific shRNA showed mixed impacts (b), whereas shRNAs simultaneously targeting both P1 and P2 HNF4α showed a primarily negative impact on cell proliferation (c, d). Moreover, a stronger negative impact was associated with increased copy number, both for the P1- HNF4α specific shRNA (P = 0.04, Spearman’s correlation (r)) and for all shRNAs (P = 0.01, Spearman’s correlation P values for individual shRNAs summarized by the Fisher’s combined probability test).
Extended Data Figure 9 Consensus matrices, the empirical cumulative distribution function plot and core sample identification.
a, Consensus matrices of the 90 CRC samples for k = 2 to k = 8. The consensus matrices show the robustness of the discovered clusters to sampling variability (resampling 80% samples) for cluster numbers k = 2 to 8. In each consensus matrix, both the rows and the columns were indexed with the same sample order and samples belonging to the same cluster frequently are adjacent to each other. For each pair of samples, a consensus index, which is the percentage of times they belong to the same cluster during 1,000 runs of the clustering algorithm based on resampling was calculated. The consensus index for each pair of samples was represented by colour gradient from white (0%) to red (100%) in the consensus matrix. b, Cumulative distribution function (CDF) plots corresponding to the consensus matrices for k = 2 to k = 8. This plot shows the cumulative distribution of the entries of the consensus matrices within the 0–1 range. Skew towards 0 and 1 indicates good clustering. As k increases, the area under the CDF is hypothesized to increase markedly until k reaches the ktrue. In this case, 7 was considered as ktrue because the change of the area under the CDF was close to zero when k increased from 7 to 8. c, Silhouette plot for core sample identification. For each sample (y axis), the silhouette width (x axis) compares its similarity to its assigned class and to any other classes. Samples with higher similarity to their assigned class than to any other classes will get positive silhouette width score and be selected as core samples.
a, The number of signature proteins for each subtype. For a given subtype, the red circle represents proteins that were different in abundance between the subtype and all other subtypes, the green circle represents proteins that were different in abundance between the subtype and normal colon tissues. The intersection between red and green circles contains the signature proteins for each subtype. b, Visualizing subtype-C-signature proteins in NetGestalt. Proteins in the iRef protein–protein interaction network are placed in a linear order together with the hierarchical modular organization of the network. Alternating bar colours (green and orange) are used to distinguish neighbouring modules. Proteins in the up and down signatures of subtype C were visualized as two separate tracks below the network modules, where each bar represents a protein. These proteins are not randomly distributed in the network. Highlighted by red or blue arrows are four Network modules (I, IV, V, VI) significantly enriched with up-signature proteins and two modules (II and III) significantly enriched with down-signature proteins (adjusted p value <0.01). c, d, Heat maps depicting relative abundance of down- and up-signature proteins of subtype C in modules III and I, respectively. Tumours are displayed as rows, grouped by normal controls (N) and proteomic subtypes (A–E) as indicated by different side bar colours. Proteins are displayed as columns. e, f, Network diagrams depicting the interaction of down- and up-signature proteins of subtype C in modules III and I, respectively. Node and node-border colours represent relatively higher or lower abundance in the subtype compared to other subtypes and normal colon tissues, respectively. Red and blue in the heat maps and the network diagrams represent relatively higher or lower abundance, respectively.
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Zhang, B., Wang, J., Wang, X. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014). https://doi.org/10.1038/nature13438
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