Analysis at the functional region level allows us to gain novel insights from pharmacogenomics data.

<p>(a, b) Mapping of the different ERBB3 functions to specific regions of the protein. Each functional relationship can be associated to a specific domain or intrinsically disordered region in ERBB3. For example, EGF receptor domains (red boxes in (b)) mediate physical interactions between ERBB3 and EGFR and NRG1 (red edges in (a)). (c) Methods focusing at the whole-protein level can not find any association between ERBB3 mutations and the activity of PF2341066. (d) Mutations altering specifically the N-terminal EGF receptor are associated to lower drug activity. (e) Mutations affecting another PFR in ERBB3, its kinase domain, and that, thus, are mainly affecting other functional regions, are not associated to any changes in drug activity. (f), Venn diagram showing the different thresholds that we have established in order to minimize false positives. We only kept PFRs with (I) p<0.001 when compared to cell lines with no mutation in the protein, (II) p<0.05 when compared to cell lines with mutations in other regions of the same protein and (III) with p>0.01 at the protein level.</p

Minimal evolution phylogeny of CBM_48 containing proteins

Alignment: hmmalign; pairwise distances: Tree-Puzzle; tree inference: FastM

Analysis of Individual Protein Regions Provides Novel Insights on Cancer Pharmacogenomics

<div><p>The promise of personalized cancer medicine cannot be fulfilled until we gain better understanding of the connections between the genomic makeup of a patient's tumor and its response to anticancer drugs. Several datasets that include both pharmacologic profiles of cancer cell lines as well as their genomic alterations have been recently developed and extensively analyzed. However, most analyses of these datasets assume that mutations in a gene will have the same consequences regardless of their location. While this assumption might be correct in some cases, such analyses may miss subtler, yet still relevant, effects mediated by mutations in specific protein regions. Here we study such perturbations by separating effects of mutations in different protein functional regions (PFRs), including protein domains and intrinsically disordered regions. Using this approach, we have been able to identify 171 novel associations between mutations in specific PFRs and changes in the activity of 24 drugs that couldn't be recovered by traditional gene-centric analyses. Our results demonstrate how focusing on individual protein regions can provide novel insights into the mechanisms underlying the drug sensitivity of cancer cell lines. Moreover, while these new correlations are identified using only data from cancer cell lines, we have been able to validate some of our predictions using data from actual cancer patients. Our findings highlight how gene-centric experiments (such as systematic knock-out or silencing of individual genes) are missing relevant effects mediated by perturbations of specific protein regions. All the associations described here are available from <a href="http://www.cancer3d.org" target="_blank">http://www.cancer3d.org</a>.</p></div

PFR perturbations identified using data from cell lines predict the survival of patients treated with Irinotecan.

<p>(a) Proteins with PFR associated to Irinotecan resistance can not be used to successfully stratify cancer patients treated with this drug, as there are no differences between patients with mutations in such proteins (gray) and those without them (black) (b) Specific PFR in these proteins do predict the outcome of cancer patients. Patients with mutations altering the PFRs found using CCLE (red) have worse outcomes that those with mutations in other regions of the same protein (green) or no mutations (black).</p

Independent domain combination evolution under an unweighted parsimony model.

<p>The histogram in <b>A</b> shows the sum for reappearing domains versus the number of reappearances. <b>B</b> is a comparison between the sum of domains that appear only once versus the sum of domains that appear more than once.</p

Metazoan core domain combinations.

<p>The 9 domain combinations exclusively found in all 48 animal genomes analyzed. For a complete list, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701.s014" target="_blank">Table S8</a>.</p

Parallel evolution of the K Homology (KH)∼DEAD/DEAH box helicase combination between Bilateria and <i>Micromonas</i> (a group of green algae).

<p>The complete diagram on which this simplified version is based is available in the supplementary materials.</p

Overview of the current model of eukaryote evolution.

<p>The six “supergroups”—Opisthokonta, Amoebozoa, Archaeplastida, Chromalveolata, Rhizaria, and Excavata—are shown (the placement of Excavata is under debate) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-Hampl1" target="_blank">[21]</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-Burki1" target="_blank">[23]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-CavalierSmith1" target="_blank">[25]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-Halanych1" target="_blank">[29]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-James1" target="_blank">[36]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-Hibbett1" target="_blank">[37]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-ShalchianTabrizi1" target="_blank">[53]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi.1002701-Roger1" target="_blank">[54]</a>.</p

Average ratios between the numbers of domain combinations and (number of domains)² for select groups of organisms.

<p>Standard deviations are shown as error bars. The asterix is used to indicate the results for Deuterostoma under exclusion of the amphioxus <i>Branchiostoma floridae</i> genome. The colors used correspond to the colors in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002701#pcbi-1002701-g001" target="_blank">Figure 1</a>.</p

Using complimentary datasets to validate some of the predictions by e-Drug.

<p>(a) Missense mutations in PIK3CA can have opposite effects in terms of AEW541 activity depending on the PFR affected. Mutations in the p85-binding and PIK accessory domains are associated with lower and higher drug activities respectively (upper panel). By integrating our analysis with proteomics data from TCPA we have been able to propose a mechanism for that. It appears that IRS1 protein expression is lower in cells with p85-binding mutations, but higher in those with PIK mutations (second panel). Moreover, Akt1 phosphorylation levels are higher in cell lines with p85-binding domain mutations (two lower panels). (b) Proposed mechanisms for the two PFR-AEW541 associations. AEW541 inhibits the kinase domain of IGF1R (upper blue protein). In those cell lines with mutations in the PIK domain of PIK3CA (shown in blue PIK3CA's structure), there is a gain of interaction between this protein and IRS1 (I). This will likely increase the signaling through IGF1R (II), explaining why cell lines with mutations in this domain are more sensitive to the inhibition of this receptor. On the other hand, cell lines with mutations in the p85-binding domain (shown in red in PIK3CA's structure) have lower IRS1 expression and higher AKT1 phosphorylation levels. Together, this suggests that PIK3CA is active in this cell lines independently of its interaction with extracellular receptors, signaling directly downstream towards AKT1 (III). This would explain why these cells are resistant to AEW541.</p