Faculty Bibliography

Advances in mass spectrometry-based methods have enabled deeper biological insights using proteomics and metabolomics approaches. This has also resulted in improvements in our ability to compare and correlate these data with other 'omics methods such as genomics and transcriptomics-thus creating the new field of multi-omics. In this session, experts will present their work in the area of multi-omics research. Robert Hettich (Oak Ridge National Laboratory) will present his experiences with integrating advanced mass spectrometry technology and meta-omics analysis for characterizing complex microbiomes. Kelly Ruggles (NYU Langone Health) will discuss ways to integrate the results from diverse experiments and develop complementary statistical analysis methods for large comprehensive proteogenomics datasets. Ewy Mathe (Ohio State University) will cover analytical methods in genomics, epigenomics, and metabolomics to characterize disease biomarkers and therapeutic targets. Lastly, Tejaswini Mis-hra (Stanford University) will tell of her work in multi-omics profiling. The presentations will be followed by a panel discussion that will address challenges and solutions for adopting multi-omics methods in core facility settings

The integration of mass spectrometry-based proteomics with next-generation DNA and RNA sequencing profiles tumors more comprehensively. Here this "proteogenomics" approach was applied to 122 treatment-naive primary breast cancers accrued to preserve post-translational modifications, including protein phosphorylation and acetylation. Proteogenomics challenged standard breast cancer diagnoses, provided detailed analysis of the ERBB2 amplicon, defined tumor subsets that could benefit from immune checkpoint therapy, and allowed more accurate assessment of Rb status for prediction of CDK4/6 inhibitor responsiveness. Phosphoproteomics profiles uncovered novel associations between tumor suppressor loss and targetable kinases. Acetylproteome analysis highlighted acetylation on key nuclear proteins involved in the DNA damage response and revealed cross-talk between cytoplasmic and mitochondrial acetylation and metabolism. Our results underscore the potential of proteogenomics for clinical investigation of breast cancer through more accurate annotation of targetable pathways and biological features of this remarkably heterogeneous malignancy.

Knowledge of fundamental differences between breast cancer subtypes has driven therapeutic advances; however, basal-like breast cancer (BLBC) remains clinically intractable. Because BLBC exhibits alterations in DNA repair enzymes and cell-cycle checkpoints, elucidation of factors enabling the genomic instability present in this subtype has the potential to reveal novel anti-cancer strategies. Here, we demonstrate that BLBC is especially sensitive to suppression of iron-sulfur cluster (ISC) biosynthesis and identify DNA polymerase epsilon (POLE) as an ISC-containing protein that underlies this phenotype. In BLBC cells, POLE suppression leads to replication fork stalling, DNA damage, and a senescence-like state or cell death. In contrast, luminal breast cancer and non-transformed mammary cells maintain viability upon POLE suppression but become dependent upon an ATR/CHK1/CDC25A/CDK2 DNA damage response axis. We find that CDK1/2 targets exhibit hyperphosphorylation selectively in BLBC tumors, indicating that CDK2 hyperactivity is a genome integrity vulnerability exploitable by targeting POLE.

The relative contributions of genetic and environmental factors to variation in immune responses are still poorly understood. Here, we performed a deep phenotypic analysis of immunological parameters of laboratory mice released into an outdoor enclosure, carrying susceptibility genes (Nod2 and Atg16l1) implicated in the development of inflammatory bowel diseases. Variations of immune cell populations were largely driven by environment, whereas cytokine production in response to stimulation was affected more by genetic mutations. Multi-omic models identified transcriptional signatures associated with differences in T cell populations. Subnetworks associated with responses against Clostridium perfringens, Candida albicans and Bacteroides vulgatus were also coupled with rewilding. Hence, exposing laboratory mice carrying different genetic mutations to a natural environment uncovered important contributors to immune variation

Multiple sclerosis (MS) is a neuroinflammatory disorder resulting from infiltration of T cells into the central nervous system (CNS) and demyelination of neurons. Since T follicular helper (TFH) cells are associated with MS relapse, their selective inhibition could be an ideal therapeutic. TFH cells require transcription factor BCL6 and active mTORC1/2 for development. When mTORC is active, cap-binding protein eukaryotic translation initiation factor 4E (eIF4E) initiates translation of selective mRNAs. However, the requirement of eIF4E for translation of mRNAs necessary for TFH cell development has not been investigated. Disruption of eIF4E binding to the 5' mRNA cap with drug 4EGI-1 inhibits TFH and germinal center (GC) B cell development while having no effect on differentiation and effector function of TH1, TH2, TH17, or Tregs. Silencing of eIF4E in only CD4 T cells is sufficient to inhibit their formation of TFH cells. We used polysome profiling to determine which mRNAs are selectively translated by eIF4E and identified salient programs regulated by transcription (BCL6, NFAT) and costimulation (CD28, SLAM). eIF4E is required for translation of BCL6 in human lymph node TFH and GC B cells. Administration of 4EGI-1 during experimental autoimmune encephalitis (EAE) results in significantly decreased infiltration of CD4 T cells in the CNS, demyelination, and clinical score. Further, 4EGI-1 treatment following initiation of symptoms results in rapid improvement of symptoms and partial remission earlier than vehicle-treated animals. Thus, eIF4E is required for differentiation of TFH cells and pathogenesis of autoimmune encephalitis, and 4EGI-1 represents a potential therapeutic.

BACKGROUND:Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow. RESULTS:We present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model. CONCLUSIONS:Hypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster .

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

Melanoma brain metastasis is the largest cause of melanoma morbidity and mortality, and melanoma has the highest rate of brain metastasis of any cancer. The mechanisms that mediate melanoma brain metastasis remain poorly understood. We characterized patient-derived Short-Term Cultures (STCs) as a novel model system for the study of melanoma brain metastasis. Unbiased proteomics analysis of STCs revealed striking alterations in brain metastasis vs non-brain metastasis derived STCs in proteins related to neurodegeneration. Through in-vivo assays, we show that loss of Amyloid Precursor Protein (APP) in melanoma cells dramatically inhibits melanoma brain metastasis formation without affecting metastasis to other organs and that amyloid beta is the form of APP critically required for melanoma brain metastasis. Additionally, we demonstrate that APP is required for late growth and survival of melanoma cells in the brain parenchyma. Furthermore, we demonstrate that melanoma-derived amyloid beta polarizes astrocytes to an anti-inflammatory secretory phenotype that inhibits microglial phagocytosis of melanoma cells. Finally, we show that treatment of mice with a beta secretase inhibitor (LY2886721), which prevents amyloid beta production, decreases brain metastatic burden. Our results demonstrate a critical role for amyloid beta in melanoma brain metastasis, establish a novel connection between brain metastasis and neurodegenerative pathologies, and show that amyloid beta is a promising therapeutic target for brain metastasis treatment. Studies to further characterize how amyloid beta acts in the melanoma brain metastasis microenvironment are currently underway