Faculty Bibliography

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

The genomes of virtually all organisms contain repetitive sequences that are generated by the activity of transposable elements (transposons). Transposons are mobile genetic elements that can move from one genomic location to another; in this process, they amplify and increase their presence in genomes, sometimes to very high copy numbers. In this Review we discuss new evidence and ideas that the activity of retrotransposons, a major subgroup of transposons overall, influences and even promotes the process of ageing and age-related diseases in complex metazoan organisms, including humans. Retrotransposons have been coevolving with their host genomes since the dawn of life. This relationship has been largely competitive, and transposons have earned epithets such as 'junk DNA' and 'molecular parasites'. Much of our knowledge of the evolution of retrotransposons reflects their activity in the germline and is evident from genome sequence data. Recent research has provided a wealth of information on the activity of retrotransposons in somatic tissues during an individual lifespan, the molecular mechanisms that underlie this activity, and the manner in which these processes intersect with our own physiology, health and well-being.

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep, a package for nonparametric description and differential analysis of genome-wide data, available from Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/blacksheepr.html) and Bioconda (https://bioconda.github.io/recipes/blksheep/README.html). BlackSheep is a complementary tool to other differential expression analysis methods, which is particularly useful when analyzing small subgroups in a larger cohort.

Objective/UNASSIGNED:Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH) to identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression. Materials and Methods/UNASSIGNED:Clinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU. Results/UNASSIGNED:XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Conclusion/UNASSIGNED:Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

BACKGROUND:Retrospective study of COVID-19 positive patients treated at NYU Langone Health (NYULH). OBJECTIVE:Identify clinical markers predictive of disease severity to assist in clinical decision triage and provide additional biological insights into disease progression. METHODS:Clinical activity of 3740 de-identified patients at NYULH between January and August 2020. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to ICU. RESULTS:XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). CONCLUSIONS:Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism's proteome-even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database-as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a "species-specific" peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

Staphylococcus aureus, a common cause of serious and often fatal infections, is well-armed with secreted factors that disarm host immune defenses. Highly expressed in vivo during infection, Staphylococcal protein A (SpA) is reported to also contribute to nasal colonization that can be a prelude to invasive infection. Co-evolution with the host immune system has provided SpA with an Fc-antibody binding site, and a Fab-binding site responsible for non-immune superantigen interactions via germline-encoded surfaces expressed on many human BCRs. We wondered whether the recurrent exposures to S. aureus commonly experienced by adults, result in the accumulation of memory B-cell responses to other determinants on SpA. We therefore isolated SpA-specific class-switched memory B cells, and characterized their encoding VH : VL antibody genes. In SpA-reactive memory B cells, we confirmed a striking bias in usage for VH genes, which retain the surface that mediates the SpA-superantigen interaction. We postulate these interactions reflect co-evolution of the host immune system and SpA, which during infection results in immune recruitment of an extraordinarily high prevalence of B cells in the repertoire that subverts the augmentation of protective defenses. Herein, we provide the first evidence that human memory responses are supplemented by B-cell clones, and circulating-antibodies, that bind to SpA determinants independent of the non-immune Fc- and Fab-binding sites. In parallel, we demonstrate that healthy individuals, and patients recovering from S. aureus infection, both have circulating antibodies with these conventional binding specificities. These findings rationalize the potential utility of incorporating specially engineered SpA proteins into a protective vaccine.

Adenosine signaling plays a critical role in the maintenance of articular cartilage and may serve as a novel therapeutic for osteoarthritis (OA), a highly prevalent and morbid disease without effective therapeutics in the current market. Mice lacking adenosine A2A receptors (A2AR) develop spontaneous OA by 16 weeks of age, a finding relevant to human OA since loss of adenosine signaling due to diminished adenosine production (NT5E deficiency) also leads to development of OA in mice and humans. To better understand the mechanism by which A2AR and adenosine generation protect from OA development, we examined differential gene expression in neonatal chondrocytes from WT and A2AR null mice. Analysis of differentially expressed genes was analyzed by KEGG pathway analysis, and oPOSSUM and the flatiron database were used to identify transcription factor binding enrichment, and tissue-specific network analyses and patterns were compared to gene expression patterns in chondrocytes from patients with OA. There was a differential expression of 2211 genes (padj<0.05). Pathway enrichment analysis revealed that pro-inflammatory changes, increased metalloprotease, reduced matrix organization, and homeostasis are upregulated in A2AR null chondrocytes. Moreover, stress responses, including autophagy and HIF-1 signaling, seem to be important drivers of OA and bear marked resemblance to the human OA transcriptome. Although A2AR null mice are born with grossly intact articular cartilage, we identify here the molecular foundations for early-onset OA in these mice, further establishing their role as models for human disease and the potential use of adenosine as a treatment for human disease.

D-type cyclins are central regulators of the cell division cycle and are among the most frequently deregulated therapeutic targets in human cancer¹, but the mechanisms that regulate their turnover are still being debated^2,3. Here, by combining biochemical and genetics studies in somatic cells, we identify CRL4^AMBRA1 (also known as CRL4^DCAF3) as the ubiquitin ligase that targets all three D-type cyclins for degradation. During development, loss of Ambra1 induces the accumulation of D-type cyclins and retinoblastoma (RB) hyperphosphorylation and hyperproliferation, and results in defects of the nervous system that are reduced by treating pregnant mice with the FDA-approved CDK4 and CDK6 (CDK4/6) inhibitor abemaciclib. Moreover, AMBRA1 acts as a tumour suppressor in mouse models and low AMBRA1 mRNA levels are predictive of poor survival in cancer patients. Cancer hotspot mutations in D-type cyclins abrogate their binding to AMBRA1 and induce their stabilization. Finally, a whole-genome, CRISPR-Cas9 screen identified AMBRA1 as a regulator of the response to CDK4/6 inhibition. Loss of AMBRA1 reduces sensitivity to CDK4/6 inhibitors by promoting the formation of complexes of D-type cyclins with CDK2. Collectively, our results reveal the molecular mechanism that controls the stability of D-type cyclins during cell-cycle progression, in development and in human cancer, and implicate AMBRA1 as a critical regulator of the RB pathway.