Kelly V. Ruggles

Proteogenomics connects somatic mutations to signalling in breast cancer

Summary Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. We describe quantitative mass spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers of which 77 provided high-quality data. Integrated analyses allowed insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated against the Library of Integrated Network-based Cellular Signatures, thereby connecting CETN3 and SKP1 loss to elevated expression of EGFR, and SKP1 loss also to increased SRC. Global proteomic data confirmed a stromal-enriched group in addition to basal and luminal clusters and pathway analysis of the phosphoproteome identified a G Protein-coupled receptor cluster that was not readily identified at the mRNA level. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We demonstrate that proteogenomic analysis of breast cancer elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.

Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels

Protein abundance and phosphorylation convey important information about pathway activity and molecular pathophysiology in diseases including cancer, providing biological insight, informing drug and diagnostic development, and guiding therapeutic intervention. Analyzed tissues are usually collected without tight regulation or documentation of ischemic time. To evaluate the impact of ischemia, we collected human ovarian tumor and breast cancer xenograft tissue without vascular interruption and performed quantitative proteomics and phosphoproteomics after defined ischemic intervals. Although the global expressed proteome and most of the >25,000 quantified phosphosites were unchanged after 60 min, rapid phosphorylation changes were observed in up to 24% of the phosphoproteome, representing activation of critical cancer pathways related to stress response, transcriptional regulation, and cell death. Both pan-tumor and tissue-specific changes were observed. The demonstrated impact of pre-analytical tissue ischemia on tumor biology mandates caution in interpreting stress-pathway activation in such samples and motivates reexamination of collection protocols for phosphoprotein analysis.

Sterol and Diacylglycerol Acyltransferase Deficiency Triggers Fatty Acid-mediated Cell Death

Deletion of the acyltransferases responsible for triglyceride and steryl ester synthesis in Saccharomyces cerevisiae serves as a genetic model of diseases where lipid overload is a component. The yeast mutants lack detectable neutral lipids and cytoplasmic lipid droplets and are strikingly sensitive to unsaturated fatty acids. Expression of human diacylglycerol acyltransferase 2 in the yeast mutants was sufficient to reverse these phenotypes. Similar to mammalian cells, fatty acid-mediated death in yeast is apoptotic and presaged by transcriptional induction of stress-response pathways, elevated oxidative stress, and activation of the unfolded protein response. To identify pathways that protect cells from lipid excess, we performed genetic interaction and transcriptional profiling screens with the yeast acyltransferase mutants. We thus identified diacylglycerol kinase-mediated phosphatidic acid biosynthesis and production of phosphatidylcholine via methylation of phosphatidylethanolamine as modifiers of lipotoxicity. Accordingly, the combined ablation of phospholipid and triglyceride biosynthesis increased sensitivity to saturated fatty acids. Similarly, normal sphingolipid biosynthesis and vesicular transport were required for optimal growth upon denudation of triglyceride biosynthesis and also mediated resistance to exogenous fatty acids. In metazoans, many of these processes are implicated in insulin secretion thus linking lipotoxicity with early aspects of pancreatic β-cell dysfunction, diabetes, and the metabolic syndrome.

Phosphatidate Phosphatase Activity Plays Key Role in Protection against Fatty Acid-induced Toxicity in Yeast

The PAH1-encoded phosphatidate (PA) phosphatase in Saccharomyces cerevisiae is a pivotal enzyme that produces diacylglycerol for the synthesis of triacylglycerol (TAG) and simultaneously controls the level of PA used for phospholipid synthesis. Quantitative lipid analysis showed that the pah1Δ mutation caused a reduction in TAG mass and an elevation in the mass of phospholipids and free fatty acids, changes that were more pronounced in the stationary phase. The levels of unsaturated fatty acids in the pah1Δ mutant were unaltered, although the ratio of palmitoleic acid to oleic acid was increased with a similar change in the fatty acid composition of phospholipids. The pah1Δ mutant exhibited classic hallmarks of apoptosis in stationary phase and a marked reduction in the quantity of cytoplasmic lipid droplets. Cells lacking PA phosphatase were sensitive to exogenous fatty acids in the order of toxicity palmitoleic acid > oleic acid > palmitic acid. In contrast, the growth of wild type cells was not inhibited by fatty acid supplementation. In addition, wild type cells supplemented with palmitoleic acid exhibited an induction in PA phosphatase activity and an increase in TAG synthesis. Deletion of the DGK1-encoded diacylglycerol kinase, which counteracts PA phosphatase in controlling PA content, suppressed the defect in lipid droplet formation in the pah1Δ mutant. However, the sensitivity of the pah1Δ mutant to palmitoleic acid was not rescued by the dgk1Δ mutation. Overall, these findings indicate a key role of PA phosphatase in TAG synthesis for protection against fatty acid-induced toxicity.

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

Integrated Bottom-up and Top-down Proteomics of Patient-derived Breast Tumor Xenografts

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the “peptide-to-protein” inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0–30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0–30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

Prevalence of Sleep Duration on an Average School Night Among 4 Nationally Representative Successive Samples of American High School Students, 2007–2013

Consistency, quality, and duration of sleep are important determinants of health. We describe sleep patterns among demographically defined subgroups from the Youth Risk Behavior Surveillance System reported in 4 successive biennial representative samples of American high school students (2007 to 2013). Across the 4 waves of data collection, 6.2% to 7.7% of females and 8.0% to 9.4% of males reported obtaining 9 or more hours of sleep. Insufficient duration of sleep is pervasive among American high school students. Despite substantive public health implications, intervention research on this topic has received little attention.

A functional, genome-wide evaluation of liposensitive yeast identifies the ARE2 required for viability (ARV1) gene product as a major component of eukaryotic fatty acid resistance

Background: Obesity-related diseases result from accumulation of lipids in nonadipose tissues. Results: Mutations in 167 yeast genes confer fatty acid sensitivity. Loss of yeast and mammalian ARV1 results in pronounced lipid hypersensitivity, lipoapoptosis, and reduced triglyceride synthesis. Conclusion: 75 evolutionarily conserved components of obesity-related disorders were identified. Significance: Understanding lipid sensitivity may lead to treatment of numerous human metabolic diseases. The toxic subcellular accumulation of lipids predisposes several human metabolic syndromes, including obesity, type 2 diabetes, and some forms of neurodegeneration. To identify pathways that prevent lipid-induced cell death, we performed a genome-wide fatty acid sensitivity screen in Saccharomyces cerevisiae. We identified 167 yeast mutants as sensitive to 0.5 mm palmitoleate, 45% of which define pathways that were conserved in humans. 63 lesions also impacted the status of the lipid droplet; however, this was not correlated to the degree of fatty acid sensitivity. The most liposensitive yeast strain arose due to deletion of the “ARE2 required for viability” (ARV1) gene, encoding an evolutionarily conserved, potential lipid transporter that localizes to the endoplasmic reticulum membrane. Down-regulation of mammalian ARV1 in MIN6 pancreatic β-cells or HEK293 cells resulted in decreased neutral lipid synthesis, increased fatty acid sensitivity, and lipoapoptosis. Conversely, elevated expression of human ARV1 in HEK293 cells or mouse liver significantly increased triglyceride mass and lipid droplet number. The ARV1-induced hepatic triglyceride accumulation was accompanied by up-regulation of DGAT1, a triglyceride synthesis gene, and the fatty acid transporter, CD36. Furthermore, ARV1 was identified as a transcriptional of the protein peroxisome proliferator-activated receptor α (PPARα), a key regulator of lipid homeostasis whose transcriptional targets include DGAT1 and CD36. These results implicate ARV1 as a protective factor in lipotoxic diseases due to modulation of fatty acid metabolism. In conclusion, a lipotoxicity-based genetic screen in a model microorganism has identified 75 human genes that may play key roles in neutral lipid metabolism and disease.

Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.