Faculty Bibliography

OBJECTIVE:Whereas genetic susceptibility for systemic lupus erythematosus (SLE) has been well explored, the triggers for clinical disease flares remain elusive. To investigate relationships between microbiota community resilience and disease activity, we performed the first longitudinal analyses of lupus gut-microbiota communities. METHODS:In an observational study, taxononomic analyses, including multivariate analysis of ß-diversity, assessed time-dependent alterations in faecal communities from patients and healthy controls. From gut blooms, strains were isolated, with genomes and associated glycans analysed. RESULTS:(RG) occurred at times of high-disease activity, and were detected in almost half of patients during lupus nephritis (LN) disease flares. Whole genome sequence analysis of RG strains isolated during these flares documented 34 genes postulated to aid adaptation and expansion within a host with an inflammatory condition. Yet, the most specific feature of strains found during lupus flares was the common expression of a novel type of cell membrane-associated lipoglycan. These lipoglycans share conserved structural features documented by mass spectroscopy, and highly immunogenic repetitive antigenic-determinants, recognised by high-level serum IgG2 antibodies, that spontaneously arose, concurrent with RG blooms and lupus flares. CONCLUSIONS:Our findings rationalise how blooms of the RG pathobiont may be common drivers of clinical flares of often remitting-relapsing lupus disease, and highlight the potential pathogenic properties of specific strains isolated from active LN patients.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

PURPOSE/OBJECTIVE:Long interspersed nuclear element-1 (LINE-1 or L1) comprises 17% of the human genome. Retrotransposons may perturb gene integrity or alter gene expression by altering regulatory regions in the genome. The germline employs a number of mechanisms, including cytosine methylation, to repress retrotransposon transcription throughout most of life. Demethylation during germ cell and early embryo development de-represses retrotransposons. Intriguingly, de novo genetic variation appearing in sperm has been implicated in a number of disorders in offspring, including autism spectrum disorder, schizophrenia, and bipolar disorder. We hypothesize that human sperm exhibit de novo retrotransposition and employ a new sequencing method, single cell transposon insertion profiling by sequencing (scTIPseq) to map them in small amounts of human sperm. METHODS:Cross-sectional case-control study of sperm samples (n=10 men; ages 32-55 years old) from consenting men undergoing IVF at NYU Langone Fertility Center. scTIPseq identified novel LINE-1 insertions in individual sperm and TIPseqHunter, a custom bioinformatics pipeline, compared the architecture of sperm LINE-1 to known LINE-1 insertions from the European database of Human specific LINE-1 (L1Hs) retrotransposon insertions (euL1db). RESULTS:scTIPseq identified 17 novel insertions in sperm. New insertions were mainly intergenic or intronic. Only one sample did not exhibit new insertions. The location or number of novel insertions did not differ by paternal age. CONCLUSION/CONCLUSIONS:This study for the first time reports novel LINE-1 insertions in human sperm, demonstrating the feasibility of scTIPseq, and identifies new contributors to genetic diversity in the human germ line.

The adhesion G-protein-coupled receptor GPR133 (ADGRD1) supports growth of the brain malignancy glioblastoma. How the extracellular interactome of GPR133 in glioblastoma modulates signaling remains unknown. Here, we use affinity proteomics to identify the transmembrane protein PTK7 as an extracellular binding partner of GPR133 in glioblastoma. PTK7 binds the autoproteolytically generated N-terminal fragment of GPR133 and its expression in trans increases GPR133 signaling. This effect requires the intramolecular cleavage of GPR133 and PTK7's anchoring in the plasma membrane. PTK7's allosteric action on GPR133 signaling is additive with but topographically distinct from orthosteric activation by soluble peptide mimicking the endogenous tethered Stachel agonist. GPR133 and PTK7 are expressed in adjacent cells in glioblastoma, where their knockdown phenocopies each other. We propose that this ligand-receptor interaction is relevant to the pathogenesis of glioblastoma and possibly other physiological processes in healthy tissues.

UNLABELLED:. Surprisingly, compared with other genetic subsets, murine and human LKB1-mutant NSCLC show marked overexpression of the NAD+-catabolizing ectoenzyme, CD38 on the surface of tumor cells. Loss of LKB1 or inactivation of Salt-Inducible Kinases (SIKs)-key downstream effectors of LKB1- induces CD38 transcription induction via a CREB binding site in the CD38 promoter. Treatment with the FDA-approved anti-CD38 antibody, daratumumab, inhibited growth of LKB1-mutant NSCLC xenografts. Together, these results reveal CD38 as a promising therapeutic target in patients with LKB1 mutant lung cancer. SIGNIFICANCE/CONCLUSIONS:tumor suppressor of lung adenocarcinoma patients and are associated with resistance to current treatments. Our study identified CD38 as a potential therapeutic target that is highly overexpressed in this specific subtype of cancer, associated with a shift in NAD homeostasis.

Objective: To determine whether a machine learning model can predict patients at risk for shoulder dystocia (SD) better than estimations of birthweight (BW) alone.
Study Design: This was a retrospective analysis of 17,731 pregnant individuals from 7/2013 to 10/2018. Utilizing a machine learning model, a total of 122 binary and continuous variables were included. Baseline models were built with different sets of variables during 3 time periods: 57 in antepartum period, 96 in stage 1 and 119 in stage 2 of labor. BW was used as a proxy for estimated fetal weight (EFW) because documented assessment of EFW was not available in all cases. Two decision tree-based models, Random Forest and XGBoost, were used as predictive models and performance was evaluated with 5-fold cross validation. Area under the receiver operating characteristic curve (AUROC) and area under precision/recall curve (AUPR) were used as metrics for evaluating model performance. Mean and standard error of performance metrics were calculated.
Result(s): The cohort included 21,232 vaginal deliveries. There were 415 (1.95%) vaginal deliveries complicated by SD that occurred in 406 patients. An AUROC of 0.73 +/- 0.01 (mean +/- standard error) was achieved for RandomForest model and 0.78 +/- 0.01 for XGBoost model for BW. BW was then added as a variable to variable sets from the antepartum period and each labor stage to assess performance change. The RandomForest model predicted patients at risk for SD better than BW alone, but only the AUROC was statistically significant at all stages (p < 0.05). (Figure 1) This finding was not demonstrated in the XGBoost model.
Conclusion(s): Our machine learning model performed better in predicting SD than EFW alone (using BW as a proxy) at each time period evaluated - the antepartum period, 1st stage and 2nd stage of labor. While our results indicate that our model may enhance the prediction of SD, these findings should be validated using a more robust data set that includes documented EFW to account for the margin of error between BW and EFW. [Formula presented] [Formula presented]
Copyright

PURPOSE/OBJECTIVE:Timely and appropriate discharge placement for patients who have undergone radical cystectomy (RC) remains challenging. Our objective was to improve the discharge planning process by creating a machine learning model that helps to predict the need for non-home hospital discharge to a higher level of care. MATERIALS AND METHODS/METHODS:Patients undergoing elective radical cystectomy for bladder cancer from 2014-2019 were identified in the ACS-NSQIP database. A gradient boosted decision tree was trained on selected predischarge variables to predict discharge location, dichotomized into home and non-home. We used threshold-moving to calibrate model predictions and evaluated model performance on a testing set using receiver operating characteristic and precision recall curves. Model performance was further examined in subgroups of interest. RESULTS AND CONCLUSIONS/CONCLUSIONS:A total of 11,881 patients met inclusion criteria with a mean age of 68.6 years. 10.6% of patients undergoing RC had non-home discharges. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.80 and an average precision of 0.33. After threshold-moving, our model had a recall of 0.757 and a precision of 0.211. Top variables by importance were septic shock occurrence, ventilator-use greater than 48 hours, organ space surgical site infection and unplanned intubation. Our model shows strong performance in identifying patients who required non-home discharge to higher levels of care, outperforming commonly used clinical indices and prior work. Modern machine learning techniques may be applied to support more timely and appropriate clinical decision making.

How cells control gene expression is a fundamental question. The relative contribution of protein-level and RNA-level regulation to this process remains unclear. Here, we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate RNA and protein abundances of genes with SCNAs, we provide insights into the rules of gene regulation. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong RNA-level regulation. Notable exceptions are plasma membrane protein complex genes, which show a weak protein-level regulation and a stronger RNA-level regulation. Strikingly, we find a strong negative association between the degree of RNA-level and protein-level regulation across genes and cellular pathways. Moreover, genes participating in the same pathway show a similar degree of RNA- and protein-level regulation. Pathways including translation, splicing, RNA processing, and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger RNA-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve one predominant mechanism of gene regulation at the protein level or at the RNA level.

PURPOSE:Adjuvant immunotherapy produces durable benefit for patients with resected melanoma, but many develop recurrence and/or immune-related adverse events (irAE). We investigated whether baseline serum autoantibody (autoAb) signatures predicted recurrence and severe toxicity in patients treated with adjuvant nivolumab, ipilimumab, or ipilimumab plus nivolumab. EXPERIMENTAL DESIGN:This study included 950 patients: 565 from CheckMate 238 (408 ipilimumab versus 157 nivolumab) and 385 from CheckMate 915 (190 nivolumab versus 195 ipilimumab plus nivolumab). Serum autoAbs were profiled using the HuProt Human Proteome Microarray v4.0 (CDI Laboratories, Mayaguez, PR). Analysis of baseline differentially expressed autoAbs was followed by recurrence and severe toxicity signature building for each regimen, testing of the signatures, and additional independent validation for nivolumab using patients from CheckMate 915. RESULTS:In the nivolumab independent validation cohort, high recurrence score predicted significantly worse recurrence-free survival [RFS; adjusted HR (aHR), 3.60; 95% confidence interval (CI), 1.98-6.55], and outperformed a model composed of clinical variables including PD-L1 expression (P < 0.001). Severe toxicity score was a significant predictor of severe irAEs (aHR, 13.53; 95% CI, 2.59-86.65). In the ipilimumab test cohort, high recurrence score was associated with significantly worse RFS (aHR, 3.21; 95% CI, 1.38-7.45) and severe toxicity score significantly predicted severe irAEs (aHR, 11.04; 95% CI, 3.84-37.25). In the ipilimumab plus nivolumab test cohort, high autoAb recurrence score was associated with significantly worse RFS (aHR, 6.45; 95% CI, 1.48-28.02), and high severe toxicity score was significantly associated with severe irAEs (aHR, 23.44; 95% CI, 4.10-212.50). CONCLUSIONS:Baseline serum autoAb signatures predicted recurrence and severe toxicity in patients treated with adjuvant immunotherapy. Prospective testing of the signatures that include datasets with longer follow-up and rare but more severe toxicities will help determine their generalizability and potential clinical utility. See related commentary by Hassel and Luke, p. 3914.