Faculty Bibliography

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.

We describe design, rapid assembly, and characterization of synthetic yeast Sc2.0 chromosome VI (synVI). A mitochondrial defect in the synVI strain mapped to synonymous coding changes within PRE4 (YFR050C), encoding an essential proteasome subunit; Sc2.0 coding changes reduced Pre4 protein accumulation by half. Completing Sc2.0 specifies consolidation of 16 synthetic chromosomes into a single strain. We investigated phenotypic, transcriptional, and proteomewide consequences of Sc2.0 chromosome consolidation in poly-synthetic strains. Another "bug" was discovered through proteomic analysis, associated with alteration of the HIS2 transcription start due to transfer RNA deletion and loxPsym site insertion. Despite extensive genetic alterations across 6% of the genome, no major global changes were detected in the poly-synthetic strain "omics" analyses. This work sets the stage for completion of a designer, synthetic eukaryotic genome.

As the use of synthetic biology both in industry and in academia grows, there is an increasing need to ensure biocontainment. There is growing interest in engineering bacterial- and yeast-based safeguard (SG) strains. First-generation SGs were based on metabolic auxotrophy; however, the risk of cross-feeding and the cost of growth-controlling nutrients led researchers to look for other avenues. Recent strategies include bacteria engineered to be dependent on nonnatural amino acids and yeast SG strains that have both transcriptional- and recombinational-based biocontainment. We describe improving yeast Saccharomyces cerevisiae-based transcriptional SG strains, which have near-WT fitness, the lowest possible escape rate, and nanomolar ligands controlling growth. We screened a library of essential genes, as well as the best-performing promoter and terminators, yielding the best SG strains in yeast. The best constructs were fine-tuned, resulting in two tightly controlled inducible systems. In addition, for potential use in the prevention of industrial espionage, we screened an array of possible "decoy molecules" that can be used to mask any proprietary supplement to the SG strain, with minimal effect on strain fitness.

Mammalian genomes are replete with interspersed repeats reflecting the activity of transposable elements. These mobile DNAs are self-propagating, and their continued transposition is a source of both heritable structural variation as well as somatic mutation in human genomes. Tailored approaches to map these sequences are useful to identify insertion alleles. Here, we describe in detail a strategy to amplify and sequence long interspersed element-1 (LINE-1, L1) retrotransposon insertions selectively in the human genome, transposon insertion profiling by next-generation sequencing (TIPseq). We also report the development of a machine-learning-based computational pipeline, TIPseqHunter, to identify insertion sites with high precision and reliability. We demonstrate the utility of this approach to detect somatic retrotransposition events in high-grade ovarian serous carcinoma.

The amounts and types of available multimodal tumor data are rapidly increasing, and their integration is critical for fully understanding the underlying cancer biology and personalizing treatment. However, the development of methods for effectively integrating multimodal data in a principled manner is lagging behind our ability to generate the data. In this article, we introduce an extension to a multiview nonnegative matrix factorization algorithm (NNMF) for dimensionality reduction and integration of heterogeneous data types and compare the predictive modeling performance of the method on unimodal and multimodal data. We also present a comparative evaluation of our novel multiview approach and current data integration methods. Our work provides an efficient method to extend an existing dimensionality reduction method. We report rigorous evaluation of the method on large-scale quantitative protein and phosphoprotein tumor data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) acquired using state-of-the-art liquid chromatography mass spectrometry. Exome sequencing and RNA-Seq data were also available from The Cancer Genome Atlas for the same tumors. For unimodal data, in case of breast cancer, transcript levels were most predictive of estrogen and progesterone receptor status and copy number variation of human epidermal growth factor receptor 2 status. For ovarian and colon cancers, phosphoprotein and protein levels were most predictive of tumor grade and stage and residual tumor, respectively. When multiview NNMF was applied to multimodal data to predict outcomes, the improvement in performance is not overall statistically significant beyond unimodal data, suggesting that proteomics data may contain more predictive information regarding tumor phenotypes than transcript levels, probably due to the fact that proteins are the functional gene products and therefore a more direct measurement of the functional state of the tumor. Here, we have applied our proposed approach to multimodal molecular data for tumors, but it is generally applicable to dimensionality reduction and joint analysis of any type of multimodal data.

SLBP (stem-loop binding protein) is a highly conserved factor necessary for the processing, translation, and degradation of H2AFX and canonical histone mRNAs. We identified the F-box protein cyclin F, a substrate recognition subunit of an SCF (Skp1-Cul1-F-box protein) complex, as the G2 ubiquitin ligase for SLBP. SLBP interacts with cyclin F via an atypical CY motif, and mutation of this motif prevents SLBP degradation in G2. Expression of an SLBP stable mutant results in increased loading of H2AFX mRNA onto polyribosomes, resulting in increased expression of H2A.X (encoded by H2AFX). Upon genotoxic stress in G2, high levels of H2A.X lead to persistent gammaH2A.X signaling, high levels of H2A.X phosphorylated on Tyr142, high levels of p53, and induction of apoptosis. We propose that cyclin F co-evolved with the appearance of stem-loops in vertebrate H2AFX mRNA to mediate SLBP degradation, thereby limiting H2A.X synthesis and cell death upon genotoxic stress.

Data sharing in the field of mass spectrometry has advanced greatly thanks to innovations such as the standardized formats, data repositories and publications guidelines. However, there is currently no data-sharing mechanism that enables real-time data browsing and deep-linking on a large scale: unrestricted data-access (particularly at the quantitative level) ultimately requires the user to download a local copy of the relevant data files (e.g. in order to generate extracted ion chromatograms). In this technical resource we present a set of technologies (collectively termed OpenSlice) that enable the user to quantitatively query hundreds of hours of proteomics discovery data (i.e. non-targeted acquisition) in real time: the user is able to effectively generate XICs for arbitrary masses on the fly and across the entire dataset (so called Global Ion Chromatograms or GICs), interacting with the results through a very intuitive browser-based interface. A key design consideration underlying the OpenSlice approach is the notion that every aspect of the acquired data must be accessible through a RESTful URL-based API, up to and including individual chromatographic peaks (hence HyperPeaks). A publicly accessible demonstration of this technology based on the Clinical Proteomics Tumor Analysis Consortium (CPTAC) CompRef dataset is made available at: http://compref.fenyolab.org

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC.