A systematic atlas of chaperome deregulation topologies across the human cancer landscape

<div><p>Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro²) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.</p></div

Preferential upregulation of ATP-dependent chaperones in group 1 cancers.

<p>Analysis of differential cancer gene expression of chaperome functional subsets. <b>A.</b> Comparing upregulation and downregulation of gene expression (∆GSA) of chaperones (n = 88) and co-chaperones (n = 244) reveals general chaperome upregulation for the majority of cancers (Group 1), while a small group of Group 2 cancers do not follow this trend. Colour code indicates chaperone up-regulation of gene expression, axes represent chaperone downregulation, co-chaperone upregulation and down-regulation of gene expression. <i>s =</i> median silhouette width (k-means clustering). <b>B.</b> Box-and-whisker plots highlight fractions of differentially expressed genes in each chaperome subset for Group 1 (red) and Group 2 (blue) cancers separately (see A). Differentially expressed genes in each set were obtained by linear modelling (Limma package in R), considering genes with p value < 0.05 (Benjamini-Hochberg corrected). Box boundaries, 25% and 75% quartiles; middle horizontal line, median; whiskers, quartile boundaries for values beyond 1.5 times the interquartile range; small circle, outlier. <b>C.</b> Assessing differential expression of ATP-dependent (n = 50) vs. ATP-independent (n = 38) chaperones highlights preferential upregulation of ATP-dependent chaperones in Group 1 cancers. Group 2 cancers do not follow this trend. <i>s =</i> median silhouette width (k-means clustering). <b>D.</b> Box-and-whisker plots (as in B.) show fractions of differentially expressed genes in the two sets of ATP-dependent and ATP-independent chaperones, partitioned by Group 1 and Group 2 cancers (see C).</p

Interactome-guided topographic maps of cancer chaperome shifts.

<p><b>A.</b> Cancer-specific chaperome meta-interactome networks, collapsing network nodes and edges onto meta-nodes and meta-edges, highlight cancer-specific gene expression changes of chaperome functional families in context of connectivity within the high-confidence physical interactome network (ME-CHAP) at reduced complexity. Node size and edge thickness correspond to the number of functional family member nodes and the sum of inter-family edges, respectively. Node colour indicates combined cancer gene expression changes based on Meta-PCA derived M-scores. LUAD = lung adenocarcinoma. <b>B.</b> Projecting differential changes between cancer and normal counterpart biopsy gene expression based on Meta-PCA derived M-scores (z dimension) onto the ME-CHAP interactome derived meta-network (see A) serving as base-grid layout (x-y dimensions), we derive cancer-specific interactome-guided 3D topographic maps. LUAD = lung adenocarcinoma. Both visualisations, meta-networks (A) and 3D-topographic maps (B) are accessible through the Proteostasis Profiler (Pro²).</p

Chaperome cancer landscape profiling.

<p><b>A.</b> The human chaperome is a central PN functional arm in charge of maintaining the cellular folding environment. It comprises 332 chaperones and co-chaperones organized in 10 functional families [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.ref004" target="_blank">4</a>]. <b>B.</b> Pipeline involving (1) Gene Set Analysis (GSA), (2) Meta-PCA, a novel two-step principal component analysis (PCA)—based dimension reduction approach yielding M-scores for quantitative analysis of chaperome functional family expression changes across a compendium of TCGA solid cancer biopsy RNA-seq expression data, and (3) Polar Plots visualising contextual quantitative chaperome alterations. <b>C.</b> We connect 332 human chaperome genes (nodes) in a high-confidence literature-curated physical protein-protein interactome network (edges) and collapse nodes within functional families and edges between families into meta-nodes and meta-edges, respectively. The resulting optimized meta-networks serve as base-grid layout to enable interactome-guided chaperome landscape modeling. <b>D.</b> We use the meta-interactome-guided base grid layout (X-Y dimensions) and Meta-PCA derived M-scores, indicating cancer expression change (Z dimension), to chart 3-dimensional quantitative topographic chaperome maps. Heatmaps, polar plots, meta-networks and 3D topographic map visualisations are accessible through the Proteostasis Profiler (Pro²) web-tool.</p

Proteasome and TRiC/CCT increase in human cancers reminisces stem cell proteostasis.

<p><b>A.</b> Heatmap indicates overall gene expression changes (∆GSA) of the human proteasome (43 genes, HGNC Family ID 690) throughout 22 TCGA solid cancers. Heatmap ∆GSA values are in the interval [-1, +1], where ‘+1’ indicates significant upregulation (p value = 0), while ‘-1’ indicates significant downregulation (p value = 0) as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.g002" target="_blank">Fig 2</a>. <b>B.</b> Heatmap highlights HSP60 gene level differential expression of TRiC/CCT complex subunits throughout 22 TCGA solid cancers. Heatmap indicates significance of up- or downregulation of gene expression (<i>t</i> test) in cancer compared to matching healthy tissue (1—signed p value) in the interval [-1, +1], where ‘+1’ indicates significant upregulation (p value = 0), while ‘-1’ indicates significant downregulation (p value = 0). Blue highlights indicate Group 2 cancers (KICH, KIRC, KIRP, PCPG, and THCA).</p

Chaperome gene expression alterations in human cancers.

<p>Chaperome as compared to permutations of non-chaperome genes (<b>A</b>) and chaperome functional family (Level 2) (<b>B</b>) gene expression states in human cancer RNA-seq datasets from The Cancer Genome Atlas (TCGA) explored by Gene Set Analysis (GSA). Heatmaps indicate significance of up or down-regulation of cancer versus healthy gene expression as ∆GSA values in the interval [-1, +1], where ‘+1’ indicates significant upregulation (p value = 0), while ‘-1’ indicates significant downregulation (p value = 0). Chaperome functional families (B) are clustered by Euclidean distance (dendrograms). Bar graphs in A and B indicate functional family GSA group mean changes. Order of TCGA cancer groups (rows) in A is according to Euclidian distance of chaperome differential expression clustering (dendrogram) in B. Turquoise box highlights the human chaperome broken down into functional families in B. Yellow borders indicate marked clusters of chaperome functional family expression and separation of clusters I and II as separated by Euclidean distance clustering of TCGA cancer groups. TCGA cancer group acronyms: THYM (thymoma), ESCA (esophageal carcinoma), BRCA (breast invasive carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), KICH (kidney chromophobe), STAD (stomach adenocarcinoma), CHOL (cholangiocarcinoma), LIHC (liver hepatocellular carcinoma), PRAD (prostate adenocarcinoma), HNSC (head and neck squamous cell carcinoma), KIRP (kidney renal papillary cell carcinoma), SARC (sarcoma), UCEC (uterine corpus endometrial carcinoma), BLCA (bladder urothelial carcinoma), PAAD (pancreatic adenocarcinoma), CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma), GBM (glioblastoma multiforme), KIRC (kidney renal clear cell carcinoma), SKCM (skin cutaneous melanoma), PCPG (pheochromocytoma and paraganglioma), THCA (thyroid carcinoma).</p

Opposing chaperome deregulation in cancers and neurodegenerative diseases.

<p><b>A—B.</b> Heatmaps indicate significance of up or down-regulation of gene expression (∆GSA) of chaperome vs. non-chaperome genes (<b>A</b>) and chaperome functional families (<b>B</b>) in Alzheimer’s (AD), Huntington’s (HD), and Parkinson’s disease (PD) compared to age-matched healthy controls. Datasets: GSE5281 (AD, superior frontal gyrus) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.ref042" target="_blank">42</a>], GSE3790 (HD, nucleus caudatus) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.ref043" target="_blank">43</a>], and GSE20295 (PD, substantia nigra) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.ref044" target="_blank">44</a>]. ∆GSA values are in the interval [-1, +1], where ‘+1’ indicates significant upregulation (p value = 0), while ‘-1’ indicates significant downregulation (p value = 0) as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005890#pcbi.1005890.g002" target="_blank">Fig 2</a>. Functional families (columns) are ordered by increasing ∆GSA group mean change (bar graphs). Turquoise box highlights the human chaperome. <b>C—D.</b> Side-by-side comparison of gene expression changes (∆GSA) of chaperome vs. non-chaperome (C), and chaperome functional families (D) in cancers versus neurodegenerative diseases (NeuroD). Bar graphs show ∆GSA group mean changes in cancers (black) compared to NeuroD (grey).</p

A systematic atlas of chaperome deregulation topologies across the human cancer landscape - Table

<p>A systematic atlas of chaperome deregulation topologies across the human cancer landscape</p> - Tabl

Polar maps of chaperome shifts in human cancers.

<p>Polar plot visualization as novel quantitative and contextual representation of M-scores, new Meta-PCA derived quantitative indices of relative disease gene expression shifts of chaperome functional processes compared to normal tissue counterparts. Examples representative of Group 1 cancers, lung adenocarcinoma (LUAD) (<b>A</b>), and Group 2 cancers, pheochromocytoma and paraganglioma (PCPG) (<b>B</b>) are shown. Blue (normal) and red lines (cancer), sample means. Halos, confidence interval at the 90% quantile range (5%–95%). LUAD (lung adenocarcinoma), PCPG (pheochromocytoma and paraganglioma).</p

From hype to reality: data science enabling personalized medicine

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.status: publishe